【MasterHSC】CCI Mean Derivative Smart Strategy🧾 Strategy Description (English)
CCI Mean Slope Smart Strategy
This strategy is built on the derivative slope behavior of the Commodity Channel Index (CCI) mean line.
It identifies key turning points or trend continuations based on how the smoothed CCI (mean value) changes direction after reaching overbought or oversold zones.
Core Idea:
When the CCI mean reverses slope after exceeding ±100, it signals a potential mean reversion (range-trading opportunity).
When the CCI mean remains above +100 or below −100 with a consistent slope, it indicates a strong trending phase (momentum continuation).
The strategy dynamically adapts between these two behaviors depending on market conditions.
Modes:
🌀 Range Reversal Mode — Focuses on slope reversals after overbought/oversold conditions.
🚀 Trend Following Mode — Captures strong momentum when the CCI mean stays extended.
🧠 Auto Mode — Automatically switches between Range and Trend logic based on CCI mean volatility.
Key Features:
Dual-direction toggle: Enable or disable long/short entries independently.
Adjustable tolerance: Choose fixed or dynamic thresholds for flexibility.
Automatic mode label and visual buy/sell markers on the chart.
Pure CCI-based system — no external filters or indicators required.
Purpose:
This system is designed to reduce false signals in sideways markets while preventing missed opportunities during strong directional trends, offering a clean balance between precision and adaptability.
Биткоин (Криптовалюта)
FluxVector Liquidity Universal Trendline FluxVector Liquidity Trendline FFTL
Summary in one paragraph
FFTL is a single adaptive trendline for stocks ETFs FX crypto and indices on one minute to daily. It fires only when price action pressure and volatility curvature align. It is original because it fuses a directional liquidity pulse from candle geometry and normalized volume with realized volatility curvature and an impact efficiency term to modulate a Kalman like state without ATR VWAP or moving averages. Add it to a clean chart and use the colored line plus alerts. Shapes can move while a bar is open and settle on close. For conservative alerts select on bar close.
Scope and intent
• Markets. Major FX pairs index futures large cap equities liquid crypto top ETFs
• Timeframes. One minute to daily
• Default demo used in the publication. SPY on 30min
• Purpose. Reduce false flips and chop by gating the line reaction to noise and by using a one bar projection
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique fusion. Directional Liquidity Pulse plus Volatility Curvature plus Impact Efficiency drives an adaptive gain for a one dimensional state
• Failure mode addressed. One or two shock candles that break ordinary trendlines and saw chop in flat regimes
• Testability. All windows and gains are inputs
• Portable yardstick. Returns use natural log units and range is bar high minus low
• Protected scripts. Not used. Method disclosed plainly here
Method overview in plain language
Base measures
• Return basis. Natural log of close over prior close. Average absolute return over a window is a unit of motion
Components
• Directional Liquidity Pulse DLP. Measures signed participation from body and wick imbalance scaled by normalized volume and variance stabilized
• Volatility Curvature. Second difference of realized volatility from returns highlights expansion or compression
• Impact Efficiency. Price change per unit range and volume boosts gain during efficient moves
• Energy score. Z scores of the above form a single energy that controls the state gain
• One bar projection. Current slope extended by one bar for anticipatory checks
Fusion rule
Weighted sum inside the energy score then logistic mapping to a gain between k min and k max. The state updates toward price plus a small flow push.
Signal rule
• Long suggestion and order when close is below trend and the one bar projection is above the trend
• Short suggestion and flip when close is above trend and the one bar projection is below the trend
• WAIT is implicit when neither condition holds
• In position states end on the opposite condition
What you will see on the chart
• Colored trendline teal for rising red for falling gray for flat
• Optional projection line one bar ahead
• Optional background can be enabled in code
• Alerts on price cross and on slope flips
Inputs with guidance
Setup
• Price source. Close by default
Logic
• Flow window. Typical range 20 to 80. Higher smooths the pulse and reduces flips
• Vol window. Typical range 30 to 120. Higher calms curvature
• Energy window. Typical range 20 to 80. Higher slows regime changes
• Min gain and Max gain. Raise max to react faster. Raise min to keep momentum in chop
UI
• Show 1 bar projection. Colors for up down flat
Properties visible in this publication
• Initial capital 25000
• Base currency USD
• Commission percent 0.03
• Slippage 5
• Default order size method percent of equity value 3%
• Pyramiding 0
• Process orders on close off
• Calc on every tick off
• Recalculate after order is filled off
Realism and responsible publication
• No performance claims
• Intrabar reminder. Shapes can move while a bar forms and settle on close
• Strategy uses standard candles only
Honest limitations and failure modes
• Sudden gaps and thin liquidity can still produce fast flips
• Very quiet regimes reduce contrast. Use larger windows and lower max gain
• Session time uses the exchange time of the chart if you enable any windows later
• Past results never guarantee future outcomes
Open source reuse and credits
• None
Hyper SAR Reactor Trend StrategyHyperSAR Reactor Adaptive PSAR Strategy
Summary
Adaptive Parabolic SAR strategy for liquid stocks, ETFs, futures, and crypto across intraday to daily timeframes. It acts only when an adaptive trail flips and confirmation gates agree. Originality comes from a logistic boost of the SAR acceleration using drift versus ATR, plus ATR hysteresis, inertia on the trail, and a bear-only gate for shorts. Add to a clean chart and run on bar close for conservative alerts.
Scope and intent
• Markets: large cap equities and ETFs, index futures, major FX, liquid crypto
• Timeframes: one minute to daily
• Default demo: BTC on 60 minute
• Purpose: faster yet calmer PSAR that resists chop and improves short discipline
• Limits: this is a strategy that places simulated orders on standard candles
Originality and usefulness
• Novel fusion: PSAR AF is boosted by a logistic function of normalized drift, trail is monotone with inertia, entries use ATR buffers and optional cooldown, shorts are allowed only in a bear bias
• Addresses false flips in low volatility and weak downtrends
• All controls are exposed in Inputs for testability
• Yardstick: ATR normalizes drift so settings port across symbols
• Open source. No links. No solicitation
Method overview
Components
• Adaptive AF: base step plus boost factor times logistic strength
• Trail inertia: one sided blend that keeps the SAR monotone
• Flip hysteresis: price must clear SAR by a buffer times ATR
• Volatility gate: ATR over its mean must exceed a ratio
• Bear bias for shorts: price below EMA of length 91 with negative slope window 54
• Cooldown bars optional after any entry
• Visual SAR smoothing is cosmetic and does not drive orders
Fusion rule
Entry requires the internal flip plus all enabled gates. No weighted scores.
Signal rule
• Long when trend flips up and close is above SAR plus buffer times ATR and gates pass
• Short when trend flips down and close is below SAR minus buffer times ATR and gates pass
• Exit uses SAR as stop and optional ATR take profit per side
Inputs with guidance
Reactor Engine
• Start AF 0.02. Lower slows new trends. Higher reacts quicker
• Max AF 1. Typical 0.2 to 1. Caps acceleration
• Base step 0.04. Typical 0.01 to 0.08. Raises speed in trends
• Strength window 18. Typical 10 to 40. Drift estimation window
• ATR length 16. Typical 10 to 30. Volatility unit
• Strength gain 4.5. Typical 2 to 6. Steepness of logistic
• Strength center 0.45. Typical 0.3 to 0.8. Midpoint of logistic
• Boost factor 0.03. Typical 0.01 to 0.08. Adds to step when strength rises
• AF smoothing 0.50. Typical 0.2 to 0.7. Adds inertia to AF growth
• Trail smoothing 0.35. Typical 0.15 to 0.45. Adds inertia to the trail
• Allow Long, Allow Short toggles
Trade Filters
• Flip confirm buffer ATR 0.50. Typical 0.2 to 0.8. Raise to cut flips
• Cooldown bars after entry 0. Typical 0 to 8. Blocks re entry for N bars
• Vol gate length 30 and Vol gate ratio 1. Raise ratio to trade only in active regimes
• Gate shorts by bear regime ON. Bear bias window 54 and Bias MA length 91 tune strictness
Risk
• TP long ATR 1.0. Set to zero to disable
• TP short ATR 0.0. Set to 0.8 to 1.2 for quicker shorts
Usage recipes
Intraday trend focus
Confirm buffer 0.35 to 0.5. Cooldown 2 to 4. Vol gate ratio 1.1. Shorts gated by bear regime.
Intraday mean reversion focus
Confirm buffer 0.6 to 0.8. Cooldown 4 to 6. Lower boost factor. Leave shorts gated.
Swing continuation
Strength window 24 to 34. ATR length 20 to 30. Confirm buffer 0.4 to 0.6. Use daily or four hour charts.
Properties visible in this publication
Initial capital 10000. Base currency USD. Order size Percent of equity 3. Pyramiding 0. Commission 0.05 percent. Slippage 5 ticks. Process orders on close OFF. Bar magnifier OFF. Recalculate after order filled OFF. Calc on every tick OFF. No security calls.
Realism and responsible publication
No performance claims. Past results never guarantee future outcomes. Shapes can move while a bar forms and settle on close. Strategies execute only on standard candles.
Honest limitations and failure modes
High impact events and thin books can void assumptions. Gap heavy symbols may prefer longer ATR. Very quiet regimes can reduce contrast and invite false flips.
Open source reuse and credits
Public domain building blocks used: PSAR concept and ATR. Implementation and fusion are original. No borrowed code from other authors.
Strategy notice
Orders are simulated on standard candles. No lookahead.
Entries and exits
Long: flip up plus ATR buffer and all gates true
Short: flip down plus ATR buffer and gates true with bear bias when enabled
Exit: SAR stop per side, optional ATR take profit, optional cooldown after entry
Tie handling: stop first if both stop and target could fill in one bar
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
• Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
• Timeframes. One minute to daily
• Default demo used in the publication. QQQ on one hour
• Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
• What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
• Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
• Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
• Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
• Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
• Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
• Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
• Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
• Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
• Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
• Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
• Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
• The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
• Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
• Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
• Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
• When polarity flips from plus to minus, the strategy closes any long and enters a short.
• When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
• White polarity plot around the zero line
• A dotted reference line at zero named Zen
• Green background tint for positive polarity and red background tint for negative polarity
• Strategy long and short markers placed by the TradingView engine at entry and at close conditions
• No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
• Price source. Default ohlc4. Stable for noisy symbols.
• Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
• Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
• Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
• Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
• Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
• Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
• Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
• Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
• Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
• Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
• Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
• Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
• Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
• Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
• Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
• This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
• Initial capital 25000
• Base currency Default
• Default order size method percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Honest limitations and failure modes
• Past results do not guarantee future outcomes
• Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
• Gap heavy symbols may benefit from the MAD Z normalization
• Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
• Session time is the exchange time of the chart
• If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
• None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
• Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
• Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
• Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
• Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
• Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
• Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
• Initial capital 25000
• Base currency Default
• Default order size percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Dataset and sample size
• Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
• Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
• Add alertcondition lines for long, short, and exit short
• Add optional table with component readouts
• Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
Universal Regime Alpha Thermocline StrategyCurrents settings adapted for BTCUSD Daily timeframe
This description is written to comply with TradingView House Rules and Script Publishing Rules. It is self contained, in English first, free of advertising, and explains originality, method, use, defaults, and limitations. No external links are included. Nothing here is investment advice.
0. Publication mode and rationale
This script is published as Protected . Anyone can add and test it from the Public Library, yet the source code is not visible.
Why Protected
The engine combines three independent lenses into one regime score and then uses an adaptive centering layer and a thermo risk unit that share a common AAR measure. The exact mapping and interactions are the result of original research and extensive validation. Keeping the implementation protected preserves that work and avoids low effort clones that would fragment feedback and confuse users.
Protection supports a single maintained build for users. It reduces accidental misuse of internal functions outside their intended context which might lead to misleading results.
1. What the strategy does in one paragraph
Universal Regime Alpha Thermocline builds a single number between zero and one that answers a practical question for any market and timeframe. How aligned is current price action with a persistent directional regime right now. To answer this the script fuses three views of the tape. Directional entropy of up versus down closes to measure unanimity.
Convexity drift that rewards true geometric compounding and penalizes drag that comes from chop where arithmetic pace is high but growth is poor.
Tail imbalance that counts decisive bursts in one direction relative to typical bar amplitude. The three channels are blended, optionally confirmed by a higher timeframe, and then adaptively centered to remove local bias. Entries fire when the score clears an entry gate. Exits occur when the score mean reverts below an exit gate or when thermo stops remove risk. Position size can scale with the certainty of the signal.
2. Why it is original and useful
It mixes orthogonal evidence instead of leaning on a single family of tools. Many regime filters depend on moving averages or volatility compression. Here we add an information view from entropy, a growth view from geometric drift, and a structural view from tail imbalance.
The drift channel separates growth from speed. Arithmetic pace can look strong in whipsaw, yet geometric growth stays weak. The engine measures both and subtracts drag so that only sequences with compounding quality rise.
Tail counting is anchored to AAR which is the average absolute return of bars in the window. This makes the threshold self scaling and portable across symbols and timeframes without hand tuned constants.
Adaptive centering prevents the score from living above or below neutral for long stretches on assets with strong skew. It recovers neutrality while still allowing persistent regimes to dominate once evidence accumulates.
The same AAR unit used in the signal also sets stop distance and trail distance. Signal and risk speak the same language which makes the method portable and easier to reason about.
3. Plain language overview of the math
Log returns . The base series is r equal to the natural log of close divided by the previous close. Log return allows clean aggregation and makes growth comparisons natural.
Directional entropy . Inside the lookback we compute the proportion p of bars where r is positive. Binary entropy of p is high when the mix of up and down closes is balanced and low when one direction dominates. Intensity is one minus entropy. Directional sign is two times p minus one. The trend channel is zero point five plus one half times sign times intensity. It lives between zero and one and grows stronger as unanimity increases.
Convexity drift with drag . Arithmetic mean of r measures pace. Geometric mean of the price ratio over the window measures compounding. Drag is the positive part of arithmetic minus geometric. Drift raw equals geometric minus drag multiplier times drag. We then map drift through an arctangent normalizer scaled by AAR and a nonlinearity parameter so the result is stable and remains between zero and one.
Tail imbalance . AAR equals the average of the absolute value of r in the window. We count up tails where r is greater than aar_mult times AAR and down tails where r is less than minus aar_mult times AAR. The imbalance is their difference over their total, mapped to zero to one. This detects directional impulse flow.
Fusion and centering . A weighted average of the three channels yields the raw score. If a higher timeframe is requested, the same function is executed on that timeframe with lookahead off and blended with a weight. Finally we subtract a fraction of the rolling mean of the score to recover neutrality. The result is clipped to the zero to one band.
4. Entries, exits, and position sizing
Enter long when score is strictly greater than the entry gate. Enter short when score is strictly less than one minus the entry gate unless direction is restricted in inputs.
Exit a long when score falls below the exit gate. Exit a short when score rises above one minus the exit gate.
Thermo stops are expressed in AAR units. A long uses the maximum of an initial stop sized by the entry price and AAR and a trail stop that references the running high since entry with a separate multiple. Shorts mirror this with the running low. If the trail is disabled the initial stop is active.
Cooldown is a simple bar counter that begins when the position returns to flat. It prevents immediate re entry in churn.
Dynamic position size is optional. When enabled the order percent of equity scales between a floor and a cap as the score rises above the gate for longs or below the symmetric gate for shorts.
5. Inputs quick guide with recommended ranges
Every input has a tooltip in the script. The same guidance appears here for fast reading.
Core window . Shared lookback for entropy, drift, and tails. Start near 80 on daily charts. Try 60 to 120 on intraday and 80 to 200 for swing.
Entry threshold . Typical range 0.55 to 0.65 for trend following. Faster entries 0.50 to 0.55.
Exit threshold . Typical range 0.35 to 0.50. Lower holds longer yet gives back more.
Weight directional entropy . Starting value 0.40. Raise on markets with clean persistence.
Weight convexity drift . Starting value 0.40. Raise when compounding quality is critical.
Weight tail imbalance . Starting value 0.20. Raise on breakout prone markets.
Tail threshold vs AAR . Typical range 1.0 to 1.5 to count decisive bursts.
Drag penalty . Typical range 0.25 to 0.75. Higher punishes chop more.
Nonlinearity scale . Typical range 0.8 to 2.0. Larger compresses extremes.
AAR floor in percent . Typical range 0.0005 to 0.002 for liquid instruments. This stabilizes the math during quiet regimes.
Adaptive centering . Keep on for most symbols. Center strength 0.40 to 0.70.
Confirm timeframe optional . Leave empty to disable. If used, try a multiple between three and five of the chart timeframe with a blend weight near 0.20.
Dynamic position size . Enable if you want size to reflect certainty. Floor and cap define the percent of equity band. A practical band for many accounts is 0.5 to 2.
Cooldown bars after exit . Start at 3 on daily or slightly higher on shorter charts.
Thermo stop multiple . Start between 1.5 and 3.0 on daily. Adjust to your tolerance and symbol behavior.
Thermo trailing stop and Trail multiple . Trail on locks gains earlier. A trail multiple near 1.0 to 2.0 is common. You can keep trail off and let the exit gate handle exits.
Background heat opacity . Cosmetic. Set to taste. Zero disables it.
6. Properties used on the published chart
The example publication uses BTCUSD on the daily timeframe. The following Properties and inputs are used so everyone can reproduce the same results.
Initial capital 100000
Base currency USD
Order size 2 percent of equity coming from our risk management inputs.
Pyramiding 0
Commission 0.05 percent
Slippage 10 ticks in the publication for clarity. Users should introduce slippage in their own research.
Recalculate after order is filled off. On every tick off.
Using bar magnifier on. On bar close on.
Risk inputs on the published chart. Dynamic position size on. Size floor percent 2. Size cap percent 2. Cooldown bars after exit 3. Thermo stop multiple 2.5. Thermo trailing stop off. Trail multiple 1.
7. Visual elements and alerts
The score is painted as a subtle dot rail near the bottom. A background heat map runs from red to green to convey regime strength at a glance. A compact HUD at the top right shows current score, the three component channels, the active AAR, and the remaining cooldown. Four alerts are included. Long Setup and Short Setup on entry gates. Exit Long by Score and Exit Short by Score on exit gates. You can disable trading and use alerts only if you want the score as a risk switch inside a discretionary plan.
8. How to reproduce the example
Open a BTCUSD daily chart with regular candles.
Add the strategy and load the defaults that match the values above.
Set Properties as listed in section 6.(they are set by default) Confirm that bar magnifier is on and process on bar close is on.
Run the Strategy Tester. Confirm that the trade count is reasonable for the sample. If the count is too low, slightly lower the entry threshold or extend history. If the count is excessively high, raise the threshold or add a small cooldown.
9. Practical tuning recipes
Trend following focus . Raise the entry threshold toward 0.60. Raise the trend weight to 0.50 and reduce tail weight to 0.15. Keep drift near 0.35 to retain the growth filter. Consider leaving the trail off and let the exit threshold manage positions.
Breakout focus . Keep entry near 0.55. Raise tail weight to 0.35. Keep aar_mult near 1.3 so only decisive bursts count. A modest cooldown near 5 can reduce immediate false flips after the first burst bar.
Chop defense . Raise drag multiplier to 0.70. Raise exit threshold toward 0.48 to recycle capital earlier. Consider a higher cooldown, for example 8 to 12 on intraday.
Higher timeframe blend . On a daily chart try a weekly confirm with a blend near 0.20. On a five minute chart try a fifteen minute confirm. This moderates transitions.
Sizing discipline . If you want constant position size, set floor equal to cap. If you want certainty scaling, set a band like 0.5 to 2 and monitor drawdown behavior before widening it.
10. Strengths and limitations
Strengths
Self scaling unit through AAR makes the tool portable across markets and timeframes.
Blends evidence that target different failure modes. Unanimity, growth quality, and impulse flow rarely agree by chance which raises confidence when they align.
Adaptive centering reduces structural bias at the score level which helps during regime flips.
Limitations
In very quiet regimes AAR becomes small even with a floor. If your symbol is thin or gap prone, raise the floor a little to keep stops and drift mapping stable.
Adaptive centering can delay early breakout acceptance. If you miss starts, lower center strength or temporarily disable centering while you evaluate.
Tail counting uses a fixed multiple of AAR. If a market alternates between very calm and very violent weeks, a single aar_mult may not capture both extremes. Sweep this parameter in research.
The engine reacts to realized structure. It does not anticipate scheduled news or liquidity shocks. Use event awareness if you trade around releases.
11. Realism and responsible publication
No promises or projections of performance are made. Past results never guarantee future outcomes.
Commission is set to 0.05 percent per round which is realistic for many crypto venues. Adjust to your own broker or exchange.
Slippage is set at 10 in the publication . Introduce slippage in your own tests or use a percent model.
Position size should respect sustainable risk envelopes. Risking more than five to ten percent per trade is rarely viable. The example uses a fixed two percent position size.
Security calls use lookahead off. Standard candles only. Non standard chart types like Heikin Ashi or Renko are not supported for strategies that submit orders.
12. Suggested research workflow
Begin with the balanced defaults. Confirm that the trade count is sensible for your timeframe and symbol. As a rough guide, aim for at least one hundred trades across a wide sample for statistical comfort. If your timeframe cannot produce that count, complement with multiple symbols or run longer history.
Sweep entry and exit thresholds on a small grid and observe stability. Stability across windows matters more than the single best value.
Try one higher timeframe blend with a modest weight. Large weights can drown the signal.
Vary aar_mult and drag_mult together. This tunes the aggression of breakouts versus defense in chop.
Evaluate whether dynamic size improves risk adjusted results for your style. If not, set floor equal to cap for constancy.
Walk forward through disjoint segments and inspect results by regime. Bootstrapping or segmented evaluation can reveal sensitivity to specific periods.
13. How to read the HUD and heat map
The HUD presents a compact view. Score is the current fused value. Trend is the directional entropy channel. Drift is the compounding quality channel. Tail is the burst flow channel. AAR is the current unit that scales stops and the drift map. CD is the cooldown counter. The background heat is a visual aid only. It can be disabled in inputs. Green zones near the upper band show alignment among the channels. Muted colors near the mid band show uncertainty.
14. Frequently asked questions
Can I use this as a pure indicator . Yes. Disable entries by restricting direction to one side you will not trade and use the alerts as a regime switch.
Will it work on intraday charts . Yes. The AAR unit scales with bar size. You will likely reduce the core window and increase cooldown slightly.
Should I enable the adaptive trail . If you wish to lock gains sooner and accept more exits, enable it. If you prefer to let the exit gate do the heavy lifting, keep it off.
Why do I sometimes see a green background without a position . Heat expresses the score. A position also depends on threshold comparisons, direction mode, and cooldown.
Why is Order size set to one hundred percent if dynamic size is on . The script passes an explicit quantity percent on each entry. That explicit quantity overrides the property. The property is kept at one hundred percent to avoid confusion when users later disable dynamic sizing.
Can I combine this with other tools on my chart . You can, yet for publication the chart is kept clean so users and moderators can see the output clearly. In your private workspace feel free to add other context.
15. Concepts glossary
AAR . Average absolute return across the lookback. Serves as a unit for tails, drift scaling, and stops.
Directional entropy . A measure of uncertainty of up versus down closes. Low entropy paired with a directional sign signals unanimity.
Geometric mean growth . Rate that preserves the effect of compounding over many bars.
Drag . The positive difference between arithmetic pace and geometric growth. Larger drag often signals churn that looks active but fails to compound.
Thermo stops . Stops expressed in the same AAR unit as the signal. They adapt with volatility and keep risk and signal on a common scale.
Adaptive centering . A bias correction that recenters the fused score around neutral so the meter does not drift due to persistent skew.
16. Educational notice and risk statement
Markets involve risk. This publication is for education and research. It does not provide financial advice and it is not a recommendation to buy or sell any instrument. Use realistic costs. Validate ideas with out of sample testing and with conservative position sizing. Past performance never guarantees future results.
17. Final notes for readers and moderators
The goal of this strategy is clarity and portability. Clarity comes from a single score that reflects three independent features of the tape. Portability comes from self scaling units that respect structure across assets and timeframes. The publication keeps the chart clean, explains the math plainly, lists defaults and Properties used, and includes warnings where care is required. The code is protected so the implementation remains consistent for the community while the description remains complete enough for users to understand its purpose and for moderators to evaluate originality and usefulness. If you explore variants, keep them self contained, explain exactly what they contribute, publish in English first, and treat others with respect in the comments.
Load the strategy on BTCUSD daily with the defaults listed above and study how the score transitions across regimes. Then adjust one lever at a time. Observe how the trend channel, the drift channel, and the tail channel interact during starts, pauses, and reversals. Use the alerts as a risk switch inside your own process or let the built in entries and exits run if you prefer an automated study. The intent is not to promise outcomes. The intent is to give you a robust meter for regime strength that travels well across markets and helps you structure decisions with more confidence.
Thank you for your time to read all of this
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Adaptive MVRV & RSI Strategy V6 (Dynamic Thresholds)Strategy Explanation
This is an advanced Dollar-Cost Averaging (DCA) strategy for Bitcoin that aims to adapt to long-term market cycles and changing volatility. Instead of relying on fixed buy/sell signals, it uses a dynamic, weighted approach based on a combination of on-chain data and classic momentum.
Core Components:
Dual-Indicator Signal: The strategy combines two powerful indicators for a more robust signal:
MVRV Ratio: An on-chain metric to identify when Bitcoin is fundamentally over or undervalued relative to its historical cost basis.
Weekly RSI: A classic momentum indicator to gauge long-term market strength and identify overbought/oversold conditions.
Dynamic, Self-Adjusting Thresholds: The core innovation of this strategy is that it avoids fixed thresholds (e.g., "sell when RSI is 70"). Instead, the buy and sell zones are dynamically calculated based on a long-term (2-year) moving average and standard deviation of each indicator. This allows the strategy to automatically adapt to Bitcoin's decreasing volatility and changing market structure over time.
Weighted DCA (Scaling In & Out): The strategy doesn't just buy or sell a fixed amount. The size of its trades is scaled based on conviction:
Buying: As the MVRV and RSI fall deeper into their "undervalued" zones, the percentage of available cash used for each purchase increases.
Selling: As the indicators rise further into "overvalued" territory, the percentage of the current position sold also increases.
This creates an adaptive system that systematically accumulates during periods of fear and distributes during periods of euphoria, with the intensity of its actions directly tied to the extremity of market conditions.
Mutanabby_AI | Algo Pro Strategy# Mutanabby_AI | Algo Pro Strategy: Advanced Candlestick Pattern Trading System
## Strategy Overview
The Mutanabby_AI Algo Pro Strategy represents a systematic approach to automated trading based on advanced candlestick pattern recognition and multi-layered technical filtering. This strategy transforms traditional engulfing pattern analysis into a comprehensive trading system with sophisticated risk management and flexible position sizing capabilities.
The strategy operates on a long-only basis, entering positions when bullish engulfing patterns meet specific technical criteria and exiting when bearish engulfing patterns indicate potential trend reversals. The system incorporates multiple confirmation layers to enhance signal reliability while providing comprehensive customization options for different trading approaches and risk management preferences.
## Core Algorithm Architecture
The strategy foundation relies on bullish and bearish engulfing candlestick pattern recognition enhanced through technical analysis filtering mechanisms. Entry signals require simultaneous satisfaction of four distinct criteria: confirmed bullish engulfing pattern formation, candle stability analysis indicating decisive price action, RSI momentum confirmation below specified thresholds, and price decline verification over adjustable lookback periods.
The candle stability index measures the ratio between candlestick body size and total range including wicks, ensuring only well-formed patterns with clear directional conviction generate trading signals. This filtering mechanism eliminates indecisive market conditions where pattern reliability diminishes significantly.
RSI integration provides momentum confirmation by requiring oversold conditions before entry signal generation, ensuring alignment between pattern formation and underlying momentum characteristics. The RSI threshold remains fully adjustable to accommodate different market conditions and volatility environments.
Price decline verification examines whether current prices have decreased over a specified period, confirming that bullish engulfing patterns occur after meaningful downward movement rather than during sideways consolidation phases. This requirement enhances the probability of successful reversal pattern completion.
## Advanced Position Management System
The strategy incorporates dual position sizing methodologies to accommodate different account sizes and risk management approaches. Percentage-based position sizing calculates trade quantities as equity percentages, enabling consistent risk exposure across varying account balances and market conditions. This approach proves particularly valuable for systematic trading approaches and portfolio management applications.
Fixed quantity sizing provides precise control over trade sizes independent of account equity fluctuations, offering predictable position management for specific trading strategies or when implementing precise risk allocation models. The system enables seamless switching between sizing methods through simple configuration adjustments.
Position quantity calculations integrate seamlessly with TradingView's strategy testing framework, ensuring accurate backtesting results and realistic performance evaluation across different market conditions and time periods. The implementation maintains consistency between historical testing and live trading applications.
## Comprehensive Risk Management Framework
The strategy features dual stop loss methodologies addressing different risk management philosophies and market analysis approaches. Entry price-based stop losses calculate stop levels as fixed percentages below entry prices, providing predictable risk exposure and consistent risk-reward ratio maintenance across all trades.
The percentage-based stop loss system enables precise risk control by limiting maximum loss per trade to predetermined levels regardless of market volatility or entry timing. This approach proves essential for systematic trading strategies requiring consistent risk parameters and capital preservation during adverse market conditions.
Lowest low-based stop losses identify recent price support levels by analyzing minimum prices over adjustable lookback periods, placing stops below these technical levels with additional buffer percentages. This methodology aligns stop placement with market structure rather than arbitrary percentage calculations, potentially improving stop loss effectiveness during normal market fluctuations.
The lookback period adjustment enables optimization for different timeframes and market characteristics, with shorter periods providing tighter stops for active trading and longer periods offering broader stops suitable for position trading approaches. Buffer percentage additions ensure stops remain below obvious support levels where other market participants might place similar orders.
## Visual Customization and Interface Design
The strategy provides comprehensive visual customization through eight predefined color schemes designed for different chart backgrounds and personal preferences. Color scheme options include Classic bright green and red combinations, Ocean themes featuring blue and orange contrasts, Sunset combinations using gold and crimson, and Neon schemes providing high visibility through bright color selections.
Professional color schemes such as Forest, Royal, and Fire themes offer sophisticated alternatives suitable for business presentations and professional trading environments. The Custom color scheme enables precise color selection through individual color picker controls, maintaining maximum flexibility for specific visual requirements.
Label styling options accommodate different chart analysis preferences through text bubble, triangle, and arrow display formats. Size adjustments range from tiny through huge settings, ensuring appropriate visual scaling across different screen resolutions and chart configurations. Text color customization maintains readability across various chart themes and background selections.
## Signal Quality Enhancement Features
The strategy incorporates signal filtering mechanisms designed to eliminate repetitive signal generation during choppy market conditions. The disable repeating signals option prevents consecutive identical signals until opposing conditions occur, reducing overtrading during consolidation phases and improving overall signal quality.
Signal confirmation requirements ensure all technical criteria align before trade execution, reducing false signal occurrence while maintaining reasonable trading frequency for active strategies. The multi-layered approach balances signal quality against opportunity frequency through adjustable parameter optimization.
Entry and exit visualization provides clear trade identification through customizable labels positioned at relevant price levels. Stop loss visualization displays active risk levels through colored line plots, ensuring complete transparency regarding current risk management parameters during live trading operations.
## Implementation Guidelines and Optimization
The strategy performs effectively across multiple timeframes with optimal results typically occurring on intermediate timeframes ranging from fifteen minutes through four hours. Higher timeframes provide more reliable pattern formation and reduced false signal occurrence, while lower timeframes increase trading frequency at the expense of some signal reliability.
Parameter optimization should focus on RSI threshold adjustments based on market volatility characteristics and candlestick pattern timeframe analysis. Higher RSI thresholds generate fewer but potentially higher quality signals, while lower thresholds increase signal frequency with corresponding reliability considerations.
Stop loss method selection depends on trading style preferences and market analysis philosophy. Entry price-based stops suit systematic approaches requiring consistent risk parameters, while lowest low-based stops align with technical analysis methodologies emphasizing market structure recognition.
## Performance Considerations and Risk Disclosure
The strategy operates exclusively on long positions, making it unsuitable for bear market conditions or extended downtrend periods. Users should consider market environment analysis and broader trend assessment before implementing the strategy during adverse market conditions.
Candlestick pattern reliability varies significantly across different market conditions, with higher reliability typically occurring during trending markets compared to ranging or volatile conditions. Strategy performance may deteriorate during periods of reduced pattern effectiveness or increased market noise.
Risk management through stop loss implementation remains essential for capital preservation during adverse market movements. The strategy does not guarantee profitable outcomes and requires proper position sizing and risk management to prevent significant capital loss during unfavorable trading periods.
## Technical Specifications
The strategy utilizes standard TradingView Pine Script functions ensuring compatibility across all supported instruments and timeframes. Default configuration employs 14-period RSI calculations, adjustable candle stability thresholds, and customizable price decline verification periods optimized for general market conditions.
Initial capital settings default to $10,000 with percentage-based equity allocation, though users can adjust these parameters based on account size and risk tolerance requirements. The strategy maintains detailed trade logs and performance metrics through TradingView's integrated backtesting framework.
Alert integration enables real-time notification of entry and exit signals, stop loss executions, and other significant trading events. The comprehensive alert system supports automated trading applications and manual trade management approaches through detailed signal information provision.
## Conclusion
The Mutanabby_AI Algo Pro Strategy provides a systematic framework for candlestick pattern trading with comprehensive risk management and position sizing flexibility. The strategy's strength lies in its multi-layered confirmation approach and sophisticated customization options, enabling adaptation to various trading styles and market conditions.
Successful implementation requires understanding of candlestick pattern analysis principles and appropriate parameter optimization for specific market characteristics. The strategy serves traders seeking automated execution of proven technical analysis techniques while maintaining comprehensive control over risk management and position sizing methodologies.
PHANTOM STRIKE Z-4 [ApexLegion]Phantom Strike Z-4
STRATEGY OVERVIEW
This strategy represents an analytical framework using 6 detection systems that analyze distinct market dimensions through adaptive timeframe optimization. Each system targets specific market inefficiencies - automated parameter adjustment, market condition filtering, phantom strike pattern detection, SR exit management, order block identification, and volatility-aware risk management - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
SYSTEM ARCHITECTURE PHILOSOPHY
Phantom Strike Z-4 operates through 12 distinct parameter groups encompassing individual settings that allow detailed customization for different trading environments. The strategy employs modular design principles where each analytical component functions independently while contributing to unified decision-making protocols. This architecture enables traders to engage with structured market analysis through intuitive configuration options while the underlying algorithms handle complex computational processes.
The framework approaches certain aspects differently from static trading approaches by implementing real-time parameter adjustment based on timeframe characteristics, market volatility conditions, news event detection, and weekend gap analysis. During low-volatility periods where traditional strategies struggle to generate meaningful returns, Z-4's adaptive systems identify micro-opportunities through formation analysis and systematic patience protocols.
🔍WHY THESE CUSTOM SYSTEMS WERE INDEPENDENTLY DEVELOPED
The strategy approaches certain aspects differently from traditional indicator combinations through systematic development of original analytical approaches:
# 1. Auto Timeframe Optimization Module (ATOM)
Problem Identification: Standard strategies use fixed parameters regardless of timeframe characteristics, leading to over-optimization on specific timeframes and reduced effectiveness when market conditions change between different time intervals. Most retail traders manually adjust parameters when switching timeframes, creating inconsistency and suboptimal results. Traditional approaches may not account for how market noise, signal frequency, and intended holding periods differ substantially between 1-minute scalping and 4-hour swing trading environments.
Custom Solution Development: The ATOM system addresses these limitations through systematic parameter matrices developed specifically for each timeframe environment. During development, analysis indicated that 1-minute charts require aggressive profit-taking approaches due to rapid price reversals, while 15-minute charts benefit from patient position holding during trend development. The system automatically detects chart timeframe through TradingView's built-in functions and applies predefined parameter configurations without user intervention.
Timeframe-Specific Adaptations:
For ultra-short timeframe trading (1-minute charts), the system recognizes that market noise dominates price action, requiring tight stop losses (1.0%) and rapid profit realization (25% at TP1, 35% at TP2, 40% at TP3). Position sizes automatically reduce to 3% of equity to accommodate the higher trading frequency while mission duration limits to 20 bars prevent extended exposure during unsuitable conditions.
Medium timeframe configurations (5-minute and 15-minute charts) balance signal quality with execution frequency. The 15-minute configuration aims to provide a favorable combination of signal characteristics and practical execution for most retail traders. Formation thresholds increase to 2.0% for both stealth and strike ready levels, requiring stronger momentum confirmation before signal activation.
Longer timeframe adaptations (1-hour and 4-hour charts) accommodate swing trading approaches where positions may develop over multiple trading sessions. Position sizing increases to 10% of equity reflecting the reduced signal frequency and higher validation requirements typical of swing trading. Take profit targets extend considerably (TP1: 2.0%, TP2: 4.0%, TP3: 8.0%) to capture larger price movements characteristic of these timeframes.
# 2. Market Condition Filtering System (MCFS)
Problem Identification: Existing volatility filters use simple ATR calculations that may not distinguish between trending volatility and chaotic noise, potentially affecting signal quality during news events, market transitions, and unusual trading sessions. Traditional volatility measurements treat all price movement equally, whether it represents genuine trend development or random market noise caused by low liquidity or algorithmic trading activities.
Custom Solution Architecture: The MCFS addresses these limitations through multi-dimensional market analysis that examines volatility characteristics, external market influences, and temporal factors affecting trading conditions. Rather than relying solely on price-based volatility measurements, the system incorporates news event detection, weekend gap analysis, and session transition monitoring to provide systematic market state assessment.
Volatility Classification and Response Framework:
• EXTREME Volatility Conditions (>2.5x average ATR): When current volatility exceeds 250% of the recent average, the system recognizes potentially chaotic market conditions that often occur during major news events, market crashes, or significant fundamental developments. During these periods, position sizing automatically reduces by 70% while exit sensitivity increases by 50%.
• HIGH Volatility Conditions (1.8-2.5x average ATR): High volatility environments often represent strong trending conditions or elevated market activity that still maintains some predictability. Position sizing reduces by 40% while maintaining standard signal generation processes.
• NORMAL Volatility Conditions (1.2-1.8x average ATR): Normal volatility represents favorable trading conditions where technical analysis may provide reliable signals and market behavior tends to follow predictable patterns. All strategy parameters operate at standard settings.
• LOW Volatility Conditions (0.8-1.2x average ATR): Low volatility environments may present opportunities for increased position sizing due to reduced risk and improved signal characteristics. Position sizing increases by 30% while profit targets extend to capture larger movements when they occur.
• DEAD Volatility Conditions (<0.8x average ATR): When volatility falls below 80% of recent averages, the system suspends trading activity to avoid choppy, directionless market conditions that may produce unfavorable risk-adjusted returns.
# 3. Phantom Strike Detection Engine (PSDE)
Problem Identification: Traditional momentum indicators may lag market reversals by 2-4 bars and can generate signals during consolidation periods. Existing oscillator combinations may lack precision in identifying high-probability momentum shifts with adequate filtering mechanisms. Most trading systems rely on single-indicator signals or simple two-indicator confirmations that may not distinguish between genuine momentum changes and temporary market fluctuations.
Multi-Indicator Convergence System: The PSDE addresses these limitations through structured multi-indicator convergence requiring simultaneous confirmation across four independent momentum systems: SuperTrend directional analysis, MACD histogram acceleration, Parabolic SAR momentum validation, and CCI buffer zone detection. This approach recognizes that each indicator provides unique market insights, and their convergence may create different trading opportunity characteristics compared to individual signals.
Enhanced vs Phantom Mode Operation:
Enhanced mode activates when at least three of the four primary indicators align with directional bias while meeting minimum validation criteria. Enhanced mode provides more frequent signals while Phantom mode offers more selective signal generation with stricter confirmation requirements.
Phantom mode requires complete alignment across all four indicators plus additional momentum validation. All Enhanced mode criteria must be met, plus additional confirmation requirements. This stricter requirement set reduces signal frequency to 5-8 monthly but aims for higher signal quality through comprehensive multi-indicator alignment and additional momentum validation.
# 4. Smart Resistance Exit Grid (SR Exit Grid)
Problem Identification: Static take-profit levels may not account for changing market conditions and momentum strength. Traditional trailing stops may exit during strong moves or during reversals, while not distinguishing between profitable and losing position characteristics.
Systematic Holding Evaluation Framework: The SR Exit Grid operates through continuous evaluation of position viability rather than predetermined price targets through a structured 4-stage priority hierarchy:
🎯 1st Priority: Standard Take Profit processing (Highest Priority)
🔄 2nd Priority: SMART EXIT (Only when TP not executed)
⛔ 3rd Priority: SL/Emergency/Timeout Exit
🛡️ 4th Priority: Smart Low Logic (Separate Safety Safeguard)
The system employs a tpExecuted flag mechanism ensuring that only one exit type activates per bar, preventing conflicting orders and maintaining execution priority. Each stage operates independently with specific trigger conditions and risk management protocols.
Fast danger scoring evaluates immediate threats including SAR distance deterioration, momentum reversals, extreme CCI readings, volatility spikes, and price action intensity. When combined scores exceed specified thresholds (8.0+ danger with <2.0 confidence), the system triggers protective exits regardless of current profitability.
# 5. Order Block Tracking System (OBTS)
Problem Identification: Standard support/resistance levels are static and may not account for institutional order flow patterns. Traditional approaches may use horizontal lines without considering market structure evolution or mathematical price relationships.
Dynamic Channel Projection Logic: The OBTS creates dynamic order block identification using pivot point analysis with parallel channel projection based on mathematical price geometry. The system identifies significant turning points through configurable swing length parameters while maintaining historical context through consecutive pivot tracking for trend analysis.
Rather than drawing static horizontal lines, the system calculates slope relationships between consecutive pivot points and projects future support/resistance levels based on mathematical progression. This approach recognizes that institutional order flow may follow geometric patterns that can be mathematically modeled and projected forward.
# 6. Volatility-Aware Risk Management (VARM)
Problem Identification: Fixed percentage risk management may not adapt optimally during varying market volatility regimes, potentially creating conservative exits in low volatility and limited protection during high volatility periods. Traditional approaches may not scale dynamically with market conditions.
Dual-Mode Adaptive Framework: The VARM provides systematic risk scaling through dual-mode architecture offering both ATR-based dynamic adjustment and fixed percentage modes. Dynamic mode automatically scales all TP/SL levels based on current market volatility while maintaining proportional risk-reward relationships. Fixed mode provides predictable percentage-based levels regardless of volatility conditions.
Emergency protection protocols operate independently from standard risk management, providing enhanced safeguards against significant moves that exceed normal volatility expectations. The emergency system cannot be disabled and triggers at wider levels than normal stops, providing final protection when standard risk management may be insufficient during extreme market events.
## Technical Formation Analysis System
The foundation of Z-4's analytical framework rests on a structured EMA system utilizing 8, 21, and 50-period exponential moving averages that create formation structure analysis. This system differs from simple crossover signals by evaluating market geometry and momentum alignment.
Formation Gap Analysis: The formation gap measurement calculates the percentage separation between Recon Scout EMA (8-period) and Technical Support EMA (21-period) to determine market state classification. When gap percentage falls below the Stealth Mode Threshold (default 1.5%), the market enters consolidation phase requiring enhanced patience. When gap exceeds Strike Ready Threshold (1.5%), conditions become favorable for momentum-based entries.
This mathematical approach to formation analysis provides structured measurement of market transition states. During stealth mode periods, the strategy reduces entry frequency while maintaining monitoring protocols. Strike ready conditions activate increased signal sensitivity and quicker entry evaluation processes.
The Command Base EMA (50-period) provides strategic context for overall market direction and trend strength measurement. Position decisions incorporate not only immediate formation geometry but also alignment with longer-term directional bias represented by Command Base positioning relative to current price action.
🎯CORE SYSTEMS TECHNICAL IMPLEMENTATION
# SuperTrend Foundation Analysis Implementation
SuperTrend calculation provides the directional foundation through volatility-adjusted bands that adapt to current market conditions rather than using fixed parameters. The system employs configurable ATR length (default 10) and multiplier (default 3.0) to create dynamic support/resistance levels that respond to both trending and ranging market environments.
Volatility-Adjusted Band Calculation:
st_atr = ta.atr(stal)
st_hl2 = (high + low) / 2
st_ub = st_hl2 + stm * st_atr
st_lb = st_hl2 - stm * st_atr
stb = close > st and ta.rising(st, 3)
The HL2 methodology (high+low)/2 aims to provide stable price reference compared to closing prices alone, reducing sensitivity to intraday price spikes that can distort traditional SuperTrend calculations. ATR multiplication creates bands that expand during volatile periods and contract during consolidation, aiming for suitable signal sensitivity across different market conditions.
Rising/Falling Trend Confirmation: The key feature involves requiring rising/falling trend confirmation over multiple periods rather than simple price-above-band validation. This requirement screens signals that occur during SuperTrend whipsaw periods common in sideways markets. SuperTrend signals with 3-period rising confirmation help reduce false signals that occur during sideways market conditions compared to simple crossover signals.
Band Distance Validation: The system measures the distance between current price and SuperTrend level as a percentage of current price, requiring minimum separation thresholds to identify meaningful momentum rather than marginal directional changes. This validation aims to reduce signal generation during periods where price oscillates closely around SuperTrend levels, indicating indecision rather than clear directional bias.
# MACD Histogram Acceleration System - Momentum Detection
MACD analysis focuses exclusively on histogram acceleration rather than traditional line crossovers, aiming to provide earlier momentum detection. This approach recognizes that histogram acceleration may precede price acceleration by 1-2 bars, potentially offering timing benefits compared to conventional MACD applications.
Acceleration-Based Signal Generation:
mf = ta.ema(close, mfl)
ms = ta.ema(close, msl)
ml = mf - ms
msg = ta.ema(ml, msgl)
mh = ml - msg
mb = mh > 0 and mh > mh and mh > mh
The requirement for positive histogram values that increase over two consecutive periods aims to identify genuine momentum expansion rather than temporary fluctuations. This filtering approach aims to reduce false signals while maintaining signal quality.
Fast/Slow EMA Optimization: The default 12/26 EMA combination aims for intended balance between responsiveness and stability for most trading timeframes. However, the system allows customization for specific market characteristics or trading styles. Shorter settings (8/21) increase sensitivity for scalping approaches, while longer settings (16/32) provide smoother signals for swing trading applications.
Signal Line Smoothing Effects: The 9-period signal line smoothing creates histogram values that screen high-frequency noise while preserving essential momentum information. This smoothing level aims to balance signal latency and accuracy across multiple market conditions.
# Parabolic SAR Validation Framework - Momentum Verification
Parabolic SAR provides momentum validation through price separation analysis and inflection detection that may precede significant trend changes. The system requires minimum separation thresholds while monitoring SAR behavior for early reversal signals.
Separation-Based Validation:
sar = ta.sar(ss, si, sm)
sarb = close > sar and (close - sar) / close > 0.005
sardp = math.abs(close - sar) / close * 100
sariu = sarm > 0 and sarm < 0 and math.abs(sarmc) > saris
The 0.5% minimum separation requirement screens marginal directional changes that may reverse within 1-3 bars. The 0.5% minimum separation requirement helps filter out marginal directional changes.
SAR Inflection Detection: SAR inflection identification examines rate-of-change over 5-period lookback periods to detect momentum direction changes before they appear in price action. Inflection sensitivity (default 1.5) determines the magnitude of momentum change required for classification. These inflection points may precede significant price reversals by 1-2 bars, potentially providing early signals for position protection or entry timing.
Strength Classification Framework: The system categorizes SAR momentum into weak/moderate/strong classifications based on distance percentage relative to strength range thresholds. Strong momentum periods (>75% of range) receive enhanced weighting in composite calculations, while weak periods (<25%) trigger additional confirmation requirements. This classification aims to distinguish between genuine momentum moves and temporary price fluctuations.
# CCI SMART Buffer Zone System - Oscillator Analysis
The CCI SMART system represents a detailed component of the PSDE, combining multiple mathematical techniques to create modified momentum detection compared to conventional CCI applications. The system employs ALMA preprocessing, TANH normalization, and dynamic buffer zone analysis for market timing.
ALMA Preprocessing Benefits: Arnaud Legoux Moving Average preprocessing aims to provide phase-neutral smoothing that reduces high-frequency noise while preserving essential momentum information. The configurable offset (0.85) and sigma (6.0) parameters create Gaussian filter characteristics that aim to maintain signal timing while reducing unwanted signals caused by random price fluctuations.
TANH Normalization Advantages: The rational TANH approximation creates bounded output (-100 to +100) that aims to prevent extreme readings from distorting analysis while maintaining sensitivity to normal market conditions. This normalization is designed to provide consistent behavior across different volatility regimes and market conditions, addressing an aspect found in traditional CCI applications.
Rational TANH Approximation Implementation:
rational_tanh(x) =>
abs_x = math.abs(x)
if abs_x >= 4.0
x >= 0 ? 1.0 : -1.0
else
x2 = x * x
numerator = x * (135135 + x2 * (17325 + x2 * (378 + x2)))
denominator = 135135 + x2 * (62370 + x2 * (3150 + x2 * 28))
numerator / denominator
cci_smart = rational_tanh(cci / 150) * 100
The rational approximation uses polynomial coefficients that provide mathematical precision equivalent to native TANH functions while maintaining computational efficiency. The 4.0 absolute value threshold creates complete saturation at extreme values, while the polynomial series delivers smooth S-curve transformation for intermediate values.
Dynamic Buffer Zone Analysis: Unlike static support/resistance levels, the CCI buffer system creates zones that adapt to current market volatility through ALMA-calculated true range measurements. Upper and lower boundaries expand during volatile periods and contract during consolidation, providing context-appropriate entry and exit levels.
CCI Buffer System Implementation:
cci = ta.cci(close, ccil)
cci_atr = ta.alma(ta.tr, al, ao, asig)
cci_bu = low - ccim * cci_atr
cci_bd = high + ccim * cci_atr
ccitu = cci > 50 and cci > cci
CCI buffer analysis creates dynamic support/resistance zones using ALMA-smoothed true range calculations rather than fixed levels. Buffer upper and lower boundaries adapt to current market volatility through ALMA calculation with configurable offset (default 0.85) and sigma (default 6.0) parameters.
The CCI trending requirements (>50 and rising) provide directional confirmation while buffer zone analysis offers price level validation. This dual-component approach identifies both momentum direction and suitable entry/exit price levels relative to current market volatility.
# Momentum Gathering and Assessment Framework
The strategy incorporates a dual-component momentum system combining RSI and MFI calculations into unified momentum assessment with configurable suppression and elevation thresholds.
Composite Momentum Calculation:
ri = ta.rsi(close, mgp)
mi = ta.mfi(close, mip)
ci = (ri + mi) / 2
us = ci < sl // Undersupported conditions
ed = ci > dl // Elevated conditions
The composite momentum score averages RSI and MFI over configurable periods (default 14) to create unified momentum measurement that incorporates both price momentum and volume-weighted momentum. This dual-factor approach provides different momentum assessment compared to single-indicator analysis.
Suppression level identification (default 35) indicates oversold conditions where counter-trend opportunities may develop. These conditions often coincide with formation analysis showing bullish progression potential, creating enhanced-validation long entry scenarios. Elevation level detection (default 65) identifies overbought conditions suitable for either short entries or long position exits depending on overall market context.
The momentum assessment operates continuously, providing real-time context for all entry and exit decisions. Rather than using fixed thresholds, the system evaluates momentum levels relative to formation geometry and volatility conditions to determine suitable response protocols.
Composite Signal Generation Architecture:
The strategy employs a systematic scoring framework that aggregates signals from independent analytical modules into unified decision matrices through mathematical validation protocols rather than simple indicator combinations.
Multi-Group Signal Analysis Structure:
The scoring architecture operates through three analytical timeframe groups, each targeting different market characteristics and response requirements:
✅Fast Group Analysis (Immediate Response): Fast group scoring evaluates immediate market conditions requiring rapid assessment and response. SAR distance analysis measures price separation from parabolic SAR as percentage of close price, with distance ratios exceeding 120% of strength range indicating momentum exhaustion (3.0 points). SAR momentum detection captures rate-of-change over 5-period lookback, with absolute momentum exceeding 2.0% indicating notable acceleration or deceleration (1.0 point).
✅Medium Group Analysis (Signal Development): Medium group scoring focuses on signal development and confirmation through momentum indicator progression. Phantom Strike detection operates in two modes: Enhanced mode requiring 4-component confirmation awards 3.0 base points, while Phantom mode requiring complete alignment plus additional criteria awards 4.0 base points.
✅Slow Group Analysis (Strategic Context): Slow group analysis provides strategic market context through trend regime classification and structural assessment. Trend classification scoring awards top points (3.5) for optimal conditions: major trend bullish with strong trend strength (>2.0% EMA spread), 2.8 points for normal strength major trends, and proportional scoring for various trend states.
Signal Integration and Quality Assessment: The integration process combines medium group tactical scoring with 30% weighting from slow group strategic assessment, recognizing that immediate signal development should receive primary emphasis while strategic context provides important validation. Fast group danger levels operate as filtering mechanisms rather than additive scoring components.
Score normalization converts raw calculations to 10-point scales through division by total possible score (19.6) and multiplication by 10. This standardization enables consistent threshold application regardless of underlying calculation complexity while maintaining proportional relationships between different signal strength levels.
Conflict Resolution and Priority Logic:
sc = math.abs(cs_les - cs_ses) < 1.5
hqls = sql and not sc and (cs_les > cs_ses * 1.15)
hqss = sqs and not sc and (cs_ses > cs_les * 1.15)
Signal conflict detection identifies situations where competing long/short signals occur simultaneously within 1.5-point differential. During conflict periods, the system requires 15% threshold margin plus absence of conflict conditions for signal activation, screening trades during uncertain market conditions.
🧠CONFIGURATION SETTINGS & USAGE GUIDE
Understanding Parameter Categories and Their Impact
The Phantom Strike Z-4 strategy organizes its numerous parameters into 12 logical groups, each controlling specific aspects of market analysis and position management. Understanding these parameter relationships enables users to customize the strategy for different trading styles, market conditions, and risk preferences without compromising the underlying analytical framework.
Parameter Group Overview and Interaction: Parameters within the strategy do not operate in isolation. Changes to formation thresholds affect signal generation frequency, which in turn impacts intended position sizing and risk management settings. Similarly, timeframe optimization automatically adjusts multiple parameter groups simultaneously, creating coordinated system behavior rather than piecemeal modifications.
Safe Modification Ranges: Each parameter includes minimum and maximum values that prevent system instability or illogical configurations. These ranges are designed to maintain strategy behavior stability and functional operation. Operating outside these ranges may result in either excessive conservatism (missed opportunities) or excessive aggression (increased risk without proportional reward).
# Tactical Formation Parameters (Group 1) - Foundation Configuration
**EMA Period Settings and Market Response**
Recon Scout EMA (Default: 8 periods): The fastest moving average in the system, providing immediate price action response and early momentum detection. This parameter influences signal sensitivity and entry timing characteristics. Values between 5-12 periods may work across most market conditions, with specific adjustment based on trading style and timeframe preferences.
-Conservative Setting (10-12 periods): Reduces signal frequency by approximately 25% while potentially improving accuracy by 8-12%. Suitable for traders preferring fewer, higher-quality signals with reduced monitoring requirements.
-Standard Setting (8 periods): Provides balanced performance with moderate signal frequency and reasonable accuracy. Represents intended configuration for most users based on backtesting across multiple market conditions.
-Aggressive Setting (5-6 periods): Increases signal frequency by 35-40% while accepting 5-8% accuracy reduction. Appropriate for active traders comfortable with increased position monitoring and faster decision-making requirements.
Technical Support EMA (Default: 21 periods): Creates medium-term trend reference and formation gap calculations that determine market state classification. This parameter establishes the baseline for consolidation detection and momentum confirmation, influencing the strategy's approach to distinguish between trending and ranging market conditions.
Command Base EMA (Default: 50 periods): Provides strategic context and long-term trend classification that influences overall market bias and position sizing decisions. This slower moving average acts as a filter for trade direction, helping support alignment with broader market trends rather than counter-trend trading against major market movements.
**Formation Threshold Configuration**
Stealth Mode Threshold (Default: 1.5%): Defines the maximum percentage gap between Recon Scout and Technical Support EMAs that indicates market consolidation. When the gap falls below this threshold, the market enters "stealth mode" requiring enhanced patience and reduced entry frequency. This parameter influences how the strategy behaves during sideways market conditions.
-Tight Threshold (0.8-1.2%): Creates more restrictive consolidation detection, reducing entry frequency during marginal trending conditions but potentially improving accuracy by avoiding low-momentum signals.
-Standard Threshold (1.5%): Provides balanced consolidation detection suitable for most market conditions and trading styles.
-Loose Threshold (2.0-3.0%): Permits trading during moderate consolidation periods, increasing opportunity capture but accepting some reduction in signal quality during transitional market phases.
-Strike Ready Threshold (Default: 1.5%): Establishes minimum EMA separation required for momentum-based entries. When the gap exceeds this threshold, conditions become favorable for signal generation and position entry. This parameter works inversely to Stealth Mode, determining when market conditions support active trading.
# Momentum System Configuration (Group 2) - Momentum Assessment
**Oscillator Period Settings**
Momentum Gathering Period (Default: 14): Controls RSI calculation length, influencing momentum detection sensitivity and signal timing. This parameter determines how quickly the momentum system responds to price momentum changes versus how stable the momentum readings remain during normal market fluctuations.
-Fast Response (7-10 periods): Aims for rapid momentum detection suitable for scalping approaches but may generate more unwanted signals during choppy market conditions.
-Standard Response (14 periods): Provides balanced momentum measurement appropriate for most trading styles and timeframes.
-Smooth Response (18-25 periods): Creates more stable momentum readings suitable for swing trading but with delayed response to momentum changes.
-Mission Indicator Period (Default: 14): Determines MFI (Money Flow Index) calculation length, incorporating volume-weighted momentum analysis alongside price-based RSI measurements. The relationship between RSI and MFI periods affects how the composite momentum score behaves during different market conditions.
**Momentum Threshold Configuration**
-Suppression Level (Default: 35): Identifies oversold conditions indicating potential bullish reversal opportunities. This threshold determines when the momentum system signals that selling pressure may be exhausted and buying interest could emerge. Lower values create more restrictive oversold identification, while higher values increase sensitivity to potential reversal conditions.
-Dominance Level (Default: 65): Establishes overbought thresholds for potential bearish reversals or long position exit consideration. The separation between Suppression and Dominance levels creates a neutral zone where momentum conditions don't strongly favor either direction.
# Phantom Strike System Configuration (Group 3) - Core Signal Generation
**System Activation and Mode Selection**
Phantom Strike System Enable (Default: True): Activates the core signal generation methodology combining SuperTrend, MACD, SAR, and CCI confirmation requirements. Disabling this system converts the strategy to basic formation analysis without advanced momentum confirmation, substantially affecting signal characteristics while increasing frequency.
Phantom Strike Mode (Default: PHANTOM): Determines signal generation strictness through different confirmation requirements. This setting fundamentally affects trading frequency, signal accuracy, and required monitoring intensity.
ENHANCED Mode: Requires 4-component confirmation with moderate validation criteria. Suitable for active trading approaches where signal frequency balances with accuracy requirements.
PHANTOM Mode: Requires complete alignment across all indicators plus additional momentum criteria. Appropriate for selective trading approaches where signal quality takes priority over frequency.
**SuperTrend Configuration**
SuperTrend ATR Length (Default: 10): Determines volatility measurement period for dynamic band calculation. This parameter affects how quickly SuperTrend bands adapt to changing market conditions and how sensitive the trend detection becomes to short-term price movements.
SuperTrend Multiplier (Default: 3.0): Controls band width relative to ATR measurements, influencing trend change sensitivity and signal frequency. This parameter determines how much price movement is required to trigger trend direction changes.
**MACD System Parameters**
MACD Fast Length (Default: 12): Establishes responsive EMA for MACD line calculation, influencing histogram acceleration detection timing and signal sensitivity.
MACD Slow Length (Default: 26): Creates baseline EMA for MACD calculations, establishing the reference for momentum measurement.
MACD Signal Length (Default: 9): Smooths MACD line to generate histogram values used for acceleration detection.
**Parabolic SAR Settings**
SAR Start (Default: 0.02): Determines initial acceleration factor affecting early SAR behavior after trend initiation.
SAR Increment (Default: 0.02): Controls acceleration factor increases as trends develop, affecting how quickly SAR approaches price during sustained moves.
SAR Maximum (Default: 0.2): Establishes upper limit for acceleration factor, preventing rapid SAR approach speed during extended trends.
**CCI Buffer System Configuration**
CCI Length (Default: 20): Determines period for CCI calculation, affecting oscillator sensitivity and signal timing.
CCI ATR Length (Default: 5): Controls period for ALMA-smoothed true range calculations used in dynamic buffer zone creation.
CCI Multiplier (Default: 1.0): Determines buffer zone width relative to ATR calculations, affecting entry requirements and signal frequency.
⭐HOW TO USE THE STRATEGY
# Step 1: Core Parameter Setup
Technical Formation Group (g1) - Foundation Settings: The Technical Formation group provides the foundational analytical framework through 7 key parameters that influence signal generation and timeframe optimization.
Auto Optimization Controls:
enable_auto_tf = input.bool(false, "🎯 Enable Auto Timeframe Optimization")
enable_market_filters = input.bool(true, "🌪️ Enable Market Condition Filters")
Auto Timeframe Optimization activation automatically detects chart timeframe and applies configured parameter matrices developed for each time interval. When enabled, the system overrides manual settings with backtested suggested values for 1M/5M/15M/1H configurations.
Market Condition Filters enable real-time parameter adjustment based on volatility classification, news event detection, and weekend gap analysis. This system provides adaptive behavior during unusual market conditions, automatically reducing position sizes during extreme volatility and increasing exit sensitivity during news events.
# Step 2: The Momentum System Configuration
Momentum Gathering Parameters (g2): The Momentum System combines RSI and MFI calculations into unified momentum assessment with configurable thresholds for market state classification.
# Step 3: Phantom Strike System Setup
Core Detection Parameters (g3): The Phantom Strike System represents the strategy's primary signal generation engine through multi-indicator convergence analysis requiring detailed configuration for intended performance.
Phantom Strike Mode selection determines signal generation strictness. Enhanced mode requires 4-component confirmation (SuperTrend + MACD + SAR + CCI) with base scoring of 3.0 points, structured for active trading with moderate confirmation requirements. Phantom mode requires complete alignment across all indicators plus additional momentum criteria with 4.0 base scoring, creating enhanced validation signals for selective trading approaches
# Step 4: SR Exit Grid Configuration
Position Management Framework (g6): The SR Exit Grid system manages position lifecycle through progressive profit-taking and adaptive holding evaluation based on market condition analysis.
esr = input.bool(true, "Enable SR Exit Grid")
ept = input.bool(true, "Enable Partial Take Profit")
ets = input.bool(true, "Enable Technical Trailing Stop")
📊MULTI-TIMEFRAME SYSTEM & ADAPTIVE FEATURES
Auto Timeframe Optimization Architecture: The Auto Timeframe Optimization system provides automated parameter adaptation that automatically configures strategy behavior based on chart timeframe characteristics with reduced need for manual adjustment.
1-Minute Ultra Scalping Configuration:
get_1M_params() =>
StrategyParams.new(
smt = 0.8, srt = 1.0, mcb = 2, mmd = 20,
smartThreshold = 0.1, consecutiveLimit = 20,
positionSize = 3.0, enableQuickEntry = true,
ptp1 = 25, ptp2 = 35, ptp3 = 40,
tm1 = 1.5, tm2 = 3.0, tm3 = 4.5, tmf = 6.0,
isl = 1.0, esl = 2.0, tsd = 0.5, dsm = 1.5)
15-Minute Swing Trading Configuration:
get_15M_params() =>
StrategyParams.new(
smt = 2.0, srt = 2.0, mcb = 8, mmd = 100,
smartThreshold = 0.3, consecutiveLimit = 12,
positionSize = 7.0, enableQuickEntry = false,
ptp1 = 15, ptp2 = 25, ptp3 = 35,
tm1 = 4.0, tm2 = 8.0, tm3 = 12.0, tmf = 18.0,
isl = 2.0, esl = 3.5, tsd = 1.2, dsm = 2.5)
Market Condition Filter Integration:
if enable_market_filters
vol_condition = get_volatility_condition()
is_news = is_news_time()
is_gap = is_weekend_gap()
step1 = adjust_for_volatility(base_params, vol_condition)
step2 = adjust_for_news(step1, is_news)
final_params = adjust_for_gap(step2, is_gap)
Market condition filters operate in conjunction with timeframe optimization to provide systematic parameter adaptation based on both temporal and market state characteristics. The system applies cascading adjustments where each filter modifies parameters before subsequent filter application.
Volatility Classification Thresholds:
- EXTREME: >2.5x average ATR (70% position reduction, 50% exit sensitivity increase)
- HIGH: 1.8-2.5x average (40% position reduction, increased monitoring)
- NORMAL: 1.2-1.8x average (standard operations)
- LOW: 0.8-1.2x average (30% position increase, extended targets)
- DEAD: <0.8x average (trading suspension)
The volatility classification system compares current 14-period ATR against a 50-period moving average to establish baseline market activity levels. This approach aims to provide stable volatility assessment compared to simple ATR readings, which can be distorted by single large price movements or temporary market disruptions.
🖥️TACTICAL HUD INTERPRETATION GUIDE
Overview of the 21-Component Real-Time Information System
The Tactical HUD Display represents the strategy's systematic information center, providing real-time analysis through 21 distinct data points organized into 6 logical categories. This system converts complex market analysis into actionable insights, enabling traders to make informed decisions based on systematic market assessment supporting informed decision-making processes.
The HUD activates through the "Show Tactical HUD" parameter and displays continuously in the top-right corner during live trading and backtesting sessions. The organized 3-column layout presents Item, Value, and Status for each component, creating efficient information density while maintaining clear readability under varying market conditions.
# Row 1: Mission Status - Advanced Position State Management
Display Format: "LONG MISSION" | "SHORT MISSION" | "STANDBY"
Color Coding: Green (Long Active) | Red (Short Active) | Gray (Standby)
Status Indicator: ✓ (Mission Active) | ○ (No Position)
"LONG MISSION" Active State Management: Long mission status indicates the strategy currently maintains a bullish position with all systematic monitoring systems engaged in active position management mode. During this important state, the system regularly evaluates holding scores through multi-component analysis, monitors TP progression across all three target levels, tracks Smart Exit criteria through fast danger and confidence assessment, and adjusts risk management parameters based on evolving position development and changing market conditions.
"SHORT MISSION" Position Management: Short mission status reflects active bearish position management with systematic monitoring systems engaged in structured defensive protocols designed for the unique characteristics of bearish market movements. The system operates in modified inverse mode compared to long positions, monitoring for systematic downward TP progression while maintaining protective exit criteria specifically calibrated for bearish position development patterns.
"STANDBY" Strategic Market Scanning Mode: Standby mode indicates no active position exposure with all systematic analytical systems operating in scanning mode, regularly evaluating evolving market conditions for qualified entry opportunities that meet the strategy's confirmation requirements.
# Row 2: Auto Timeframe | Market Filters - System Configuration
Display Format: "1M ULTRA | ON" | "5M SCALP | OFF" | "MANUAL | ON"
Color Coding: Lime (Auto Optimization Active) | Gray (Manual Configuration)
Timeframe-Specific Configuration Indicators:
• 1M ULTRA: One-minute ultra-scalping configuration configured for rapid-fire trading with accelerated profit capture (25%/35%/40% TP distribution), conservative risk management (3% position sizing, 1.0% initial stops), and increased Smart Exit sensitivity (0.1 threshold, 20-bar consecutive limit).
• 15M SWING: Fifteen-minute swing trading configuration representing the strategy's intended performance environment, featuring conservative TP distribution (15%/25%/35%), expanded position sizing (7% allocation), extended target multipliers (4.0/8.0/12.0/18.0 ATR).
• MANUAL: User-defined parameter configuration without automatic adjustment, requiring manual modification when switching timeframes but providing full customization control for experienced traders.
Market Filter Status: ON: Real-time volatility classification and market condition adjustments modifying strategy behavior through automated parameter scaling. OFF: Standard parameter operation only without dynamic market condition adjustments.
# Row 3: Signal Mode - Sensitivity Configuration Framework
Display Format: "BALANCED" | "AGGRESSIVE"
Color Coding: Aqua (Balanced Mode) | Red (Aggressive Mode)
"BALANCED" Mode Characteristics: Balanced mode utilizes structured conservative signal sensitivity requiring enhanced verification across all analytical components before allowing signal generation. This rigorous configuration requires Medium Group scoring ≥5.5 points, Slow Group confirmation ≥3.5 points, and Fast Danger levels ≤2.0 points.
"AGGRESSIVE" Mode Characteristics: Aggressive mode strategically reduces confirmation requirements to increase signal frequency while accepting moderate accuracy reduction. Threshold requirements decrease to Medium Group ≥4.5 points, Slow Group ≥2.5 points, and Fast Danger ≤1.0 points.
# Row 4: PS Mode (Phantom Strike Mode) - Core Signal Generation Engine
Display Format: "ENHANCED" | "PHANTOM" | "DISABLED"
Color Coding: Aqua (Enhanced Mode) | Lime (Phantom Mode) | Gray (Disabled)
"ENHANCED" Mode Operation: Enhanced mode operates the structured 4-component confirmation system (SuperTrend directional analysis + MACD histogram acceleration + Parabolic SAR momentum validation + CCI buffer zone confirmation) with systematically configured moderate validation criteria, awarding 3.0 base points for signal strength calculation.
"PHANTOM" Mode Operation: Phantom mode utilizes enhanced verification requirements supporting complete alignment across all analytical indicators plus additional momentum validation criteria, awarding 4.0 base points for signal strength calculation within the selective performance framework.
# Row 5: PS Confirms (Phantom Strike Confirmations) - Real-Time Signal Development Tracking
Display Format: "ST✓ MACD✓ SAR✓ CCI✓" | Individual component status display
Color Coding: White (Component Status Text) | Dynamic Count Color (Green/Yellow/Red)
Individual Component Interpretation:
• ST✓ (SuperTrend Confirmation): SuperTrend confirmation indicates established bullish directional alignment with current price positioned above calculated SuperTrend level plus rising trend validation over the required confirmation period.
• MACD✓ (Histogram Acceleration Confirmation): MACD confirmation requires positive histogram values demonstrating clear acceleration over the specified confirmation period.
• SAR✓ (Momentum Validation Confirmation): SAR confirmation requires bullish directional alignment with minimum price separation requirements to identify meaningful momentum rather than marginal directional change.
• CCI✓ (Buffer Zone Confirmation): CCI confirmation requires trending conditions above 50 midline with momentum continuation, indicating that oscillator conditions support established directional bias.
# Row 6: Mission ROI - Performance Measurement Including All Costs
Display Format: "+X.XX%" | "-X.XX%" | "0.00%"
Color Coding: Green (Positive Performance) | Red (Negative Performance) | Gray (Breakeven)
Real ROI provides position performance measurement including detailed commission cost analysis (0.15% round-trip transaction costs), representing actual profitability rather than theoretical gains that ignore trading expenses.
# Row 7: Exit Grid + Remaining Position - Progressive Target Management
Display Format: "TP3 ✓ (X% Left)" | "TP2 ✓ (X% Left)" | "TP1 ✓ (X% Left)" | "TRACKING (X% Left)" | "STANDBY (100%)"
Color Coding: Green (TP3 Achievement) | Yellow (TP2 Achievement) | Orange (TP1 Achievement) | Aqua (Active Tracking) | Gray (No Position)
• TP1 Achievement Analysis: TP1 achievement represents initial profit capture with 20% of original position closed at first target level, supporting signal quality assessment while maintaining 80% position exposure for continued profit potential.
• TP2 Achievement Analysis: TP2 achievement indicates meaningful profit realization with cumulative 50% position closure, suggesting favorable signal development while maintaining meaningful 50% exposure for potential extended profit scenarios.
• TP3 Achievement Analysis: TP3 achievement represents notable position performance with 90% cumulative closure, suggesting favorable signal development and effective market timing.
# Row 8: Entry Signal - Signal Strength Assessment and Readiness Analysis
Display Format: "LONG READY (X.X/10)" | "SHORT READY (X.X/10)" | "WAITING (X.X/10)"
Color Coding: Lime (Long Signal Ready) | Red (Short Signal Ready) | Gray (Insufficient Signal)
Signal Strength Classification:
• High Signal Strength (8.0-10.0/10): High signal strength indicates market conditions with systematic analytical alignment supporting directional bias through confirmation across all evaluation criteria. These conditions represent optimal entry scenarios with strong analytical support.
• Strong Signal Quality (6.0-7.9/10): Strong signal quality represents solid market conditions with analytical alignment supporting directional thesis through systematic confirmation protocols. These signals meet enhanced validation requirements for quality entry opportunities.
• Moderate Signal Strength (4.5-5.9/10): Moderate signal strength indicates basic market conditions meeting minimum entry requirements through systematic confirmation satisfaction.
# Row 9: Major Trend Analysis - Strategic Direction Assessment
Display Format: "X.X% STRONG BULL" | "X.X% BULL" | "X.X% BEAR" | "X.X% STRONG BEAR" | "NEUTRAL"
Color Coding: Lime (Strong Bull) | Green (Bull) | Red (Bear) | Dark Red (Strong Bear) | Gray (Neutral)
• Strong Bull Conditions (>3.0% with Bullish Structure): Strong bull classification indicates substantial upward trend strength with EMA spread exceeding 3.0% combined with favorable bullish structure alignment. These conditions represent strong momentum environments where trend persistence may show notable probability characteristics.
• Standard Bull Conditions (1.5-3.0% with Bullish Structure): Standard bull classification represents healthy upward trend conditions with moderate momentum characteristics supporting continued bullish bias through systematic structural analysis.
# Row 10: EMA Formation Analysis - Structural Assessment Framework
Display Format: "BULLISH ADVANCE" | "BEARISH RETREAT" | "NEUTRAL"
Color Coding: Lime (Strong Bullish) | Red (Strong Bearish) | Gray (Neutral/Mixed)
• BULLISH ADVANCE Formation Analysis: Bullish Advance indicates systematic positive EMA alignment with upward structural development supporting sustained directional momentum. This formation represents favorable conditions for bullish position strategies through mathematical validation of structural strength and momentum persistence characteristics.
• BEARISH RETREAT Formation Analysis: Bearish Retreat indicates systematic negative EMA alignment with downward structural development supporting continued bearish momentum through mathematical validation of structural deterioration patterns.
# Row 11: Momentum Status - Composite Momentum Oscillator Assessment
Display Format: "XX.X | STATUS" (Composite Momentum Score with Assessment)
Color Coding: White (Score Display) | Assessment-Dependent Status Color
The Momentum Status system combines Relative Strength Index (RSI) and Money Flow Index (MFI) calculations into unified momentum assessment providing both price-based and volume-weighted momentum analysis.
• SUPPRESSED Conditions (<35 Momentum Score): SUPPRESSED classification indicates oversold market conditions where selling pressure may be reaching exhaustion levels, potentially creating favorable conditions for bullish reversal opportunities.
• ELEVATED Conditions (>65 Momentum Score): ELEVATED classification indicates overbought market conditions where buying pressure may be reaching unsustainable levels, creating potential bearish reversal scenarios.
# Row 12: CCI Information Display - Momentum Direction Analysis
Display Format: "XX.X | UP" | "XX.X | DOWN"
Color Coding: Lime (Bullish Momentum Trend) | Red (Bearish Momentum Trend)
The CCI Information Display showcases the CCI SMART system incorporating Arnaud Legoux Moving Average (ALMA) preprocessing combined with rational approximation of the hyperbolic tangent (TANH) function to achieve modified signal processing compared to traditional CCI implementations.
CCI Value Interpretation:
• Extreme Bullish Territory (>80): CCI readings exceeding +80 indicate extreme bullish momentum conditions with potential overbought characteristics requiring careful evaluation for continued position holding versus profit-taking consideration.
• Strong Bullish Territory (50-80): CCI readings between +50 and +80 indicate strong bullish momentum with favorable conditions for continued bullish positioning and standard target expectations.
• Neutral Momentum Zone (-50 to +50): CCI readings within neutral territory indicate ranging momentum conditions without strong directional bias, suitable for patient signal development monitoring.
• Strong Bearish Territory (-80 to -50): CCI readings between -50 and -80 indicate strong bearish momentum creating favorable conditions for bearish positioning while suggesting caution for bullish strategies.
• Extreme Bearish Territory (<-80): CCI readings below -80 indicate extreme bearish momentum with potential oversold characteristics creating possible reversal opportunities when combined with supportive analytical factors.
# Row 13: SAR Network - Multi-Component Momentum Analysis
Display Format: "X.XX% | BULL STRONG ↗INF" | Complex Multi-Component Analysis
Color Coding: Lime (Bullish Strong) | Green (Bullish Moderate) | Red (Bearish Strong) | Orange (Bearish Moderate) | White (Inflection Priority)
SAR Distance Percentage Analysis: The distance percentage component measures price separation from SAR level as percentage of current price, providing quantification of momentum strength through mathematical price relationship analysis.
SAR Strength Classification Framework:
• STRONG Momentum Conditions (>75% of Strength Range): STRONG classification indicates significant momentum conditions with price-SAR separation exceeding 75% of calculated strength range, representing notable directional movement with sustainability characteristics.
• MODERATE Momentum Conditions (25-75% of Range): MODERATE classification represents normal momentum development with suitable directional characteristics for standard positioning strategies and normal target expectations.
• WEAK Momentum Conditions (<25% of Range): WEAK classification indicates minimal momentum with price-SAR separation below 25% of strength range, suggesting potential reversal zones or ranging conditions unsuitable for strong directional strategies.
Inflection Detection System:
• Bullish Inflection (↗INF): Bullish inflection detection identifies moments when SAR momentum transitions from declining to rising through systematic rate-of-change analysis over 5-period lookback periods. These inflection points may precede significant bullish price reversals by 1-2 bars.
• Bearish Inflection (↘INF): Bearish inflection detection captures SAR momentum transitions from rising to declining, indicating potential bearish reversal development benefiting from prompt attention for position management evaluation.
# Row 14: VWAP Context Analysis - Institutional Volume-Weighted Price Reference
Display Format: "Daily: XXXX.XX (+X.XX%)" | "N/A (Index/Futures)"
Color Coding: Lime (Above VWAP Premium) | Red (Below VWAP Discount) | Gray (Data Unavailable)
Volume-Weighted Average Price (VWAP) provides institutional-level price reference showing mathematical average price where significant volume has transacted throughout the specified period. This calculation represents fair value assessment from institutional perspective.
• Above VWAP Conditions (✓ Status - Lime Color): Price positioning above VWAP indicates current market trading at premium to volume-weighted average, suggesting buyer willingness to pay above fair value for continued position accumulation.
• Below VWAP Conditions (✗ Status - Red Color): Price positioning below VWAP indicates current market trading at discount to volume-weighted average, creating potential value opportunities for accumulation while suggesting seller pressure exceeding buyer demand at fair value levels.
# Row 15: TP SL System Configuration - Dynamic vs Static Target Management
Display Format: "DYNAMIC ATR" | "STATIC %"
Color Coding: Aqua (Dynamic ATR Mode) | Yellow (Static Percentage Mode)
• DYNAMIC ATR Mode Analysis: Dynamic ATR mode implements systematic volatility-adaptive target management where all profit targets and stop losses automatically scale based on current market volatility through ATR (Average True Range) calculations. This approach aims to keep target levels proportionate to actual market movement characteristics rather than fixed percentages that may become unsuitable during changing volatility regimes.
• STATIC % Mode Analysis: Static percentage mode implements traditional fixed percentage targets (default 1.0%/2.5%/3.8%/4.5%) regardless of current market volatility conditions, providing predictable target levels suitable for traders preferring fixed percentage objectives without volatility-based adjustments.
# Row 16: TP Sequence Progression - Systematic Achievement Tracking
Display Format: "1 ✓ 2 ✓ 3 ○" | "1 ○ 2 ○ 3 ○" | Progressive Achievement Display
Color Coding: White text with systematic achievement progression
Status Indicator: ✓ (Achievement Confirmed) | ○ (Target Not Achieved)
• Complete Achievement Sequence (1 ✓ 2 ✓ 3 ✓): Complete sequence achievement represents significant position performance with systematic profit realization across all primary target levels, indicating favorable signal quality and effective market timing.
• Partial Achievement Analysis: Partial achievement patterns provide insight into position development characteristics and market condition assessment. TP1 achievement suggests signal timing effectiveness while subsequent target achievement depends on continued momentum development.
• No Achievement Display (1 ○ 2 ○ 3 ○): No achievement indication represents early position development phase or challenging market conditions requiring patience for target realization.
# Row 17: Mission Duration Tracking - Time-Based Position Management
Display Format: "XX/XXX" (Current Bars/Maximum Duration Limit)
Color Coding: Green (<50% Duration) | Orange (50-80% Duration) | Red (>80% Duration)
• Normal Duration Periods (Green Status <50%): Normal duration indicates position development within expected timeframes based on signal characteristics and market conditions, representing healthy position progression without time pressure concerns.
• Extended Duration Periods (Orange Status 50-80%): Extended duration indicates position development requiring longer timeframes than typical expectations, warranting increased monitoring for resolution through either target achievement or protective exit consideration.
• Critical Duration Periods (Red Status >80%): Critical duration approaches maximum holding period limits, requiring immediate resolution evaluation through either target achievement acceleration, Smart Exit activation, or systematic timeout protocols.
# Row 18: Last Exit Analysis - Historical Exit Pattern Assessment
Display Format: Exit Reason with Color-Coded Classification
Color Coding: Lime (TP Exits) | Red (Critical Exits) | Yellow (Stop Losses) | Purple (Smart Low) | Orange (Timeout/Sustained)
• Profit-Taking Exits (Lime/Green): TP1/TP2/TP3/Final Target exits indicate position management with systematic profit realization suggesting signal quality and strategy performance.
• Critical/Emergency Exits (Red): Critical and Emergency exits indicate protective system activation during adverse market conditions, showing risk management through early threat detection and systematic protective response.
• Smart Low Exits (Purple): Smart Low exits represent behavioral finance safeguards activating at -3.5% ROI threshold when emotional trading patterns may develop, aiming to reduce emotional decision-making during extended negative performance periods.
# Row 19: Fast Danger Assessment - Immediate Threat Detection System
Display Format: "X.X/10" (Danger Score out of 10)
Color Coding: Green (<3.0 Safe) | Yellow (3.0-5.0 Moderate) | Red (>5.0 High Danger)
The Fast Danger Assessment system provides real-time evaluation of immediate market threats through six independent measurement systems: SAR distance deterioration, momentum reversal detection, extreme CCI readings, volatility spike analysis, price action intensity, and combined threat evaluation.
• Safe Conditions (Green <3.0): Safe danger levels indicate stable market conditions with minimal immediate threats to position viability, enabling position holding with standard monitoring protocols.
• Moderate Concern (Yellow 3.0-5.0): Moderate danger levels indicate developing threats requiring increased monitoring and preparation for potential protective action, while not immediately demanding position closure.
• High Danger (Red >5.0): High danger levels indicate significant immediate threats requiring immediate protective evaluation and potential position closure consideration regardless of current profitability.
# Row 20: Holding Confidence Evaluation - Position Viability Assessment
Display Format: "X.X/10" (Confidence Score out of 10)
Color Coding: Green (>6.0 High Confidence) | Yellow (3.0-6.0 Moderate Confidence) | Red (<3.0 Low Confidence)
Holding Confidence evaluation provides systematic assessment of position viability through analysis of trend strength maintenance, formation quality persistence, momentum sustainability, and overall market condition favorability for continued position development.
• High Confidence (Green >6.0): High confidence indicates strong position viability with supporting factors across multiple analytical dimensions, suggesting continued position holding with extended target expectations and reduced exit sensitivity.
• Moderate Confidence (Yellow 3.0-6.0): Moderate confidence indicates suitable position viability with mixed supporting factors requiring standard position management protocols and normal exit sensitivity.
• Low Confidence (Red <3.0): Low confidence indicates deteriorating position viability with weakening supporting factors across multiple analytical dimensions, requiring increased protective evaluation and potential Smart Exit activation.
# Row 21: Volatility | Market Status - Volatility Environment & Market Filter Status
Display Format: "NORMAL | NORMAL" | "HIGH | HIGH VOL" | "EXTREME | NEWS FILTER"
Color Coding: White (Information display)
Volatility Classification Component (Left Side):
- DEAD: ATR ratio <0.8x average, minimal price movement requiring careful timing
- LOW: ATR ratio 0.8-1.2x average, stable conditions enabling position increase potential
- NORMAL: ATR ratio 1.2-1.8x average, typical market behavior with standard parameters
- HIGH: ATR ratio 1.8-2.5x average, elevated movement requiring increased caution
- EXTREME: ATR ratio >2.5x average, chaotic conditions triggering enhanced protection
Market Status Component (Right Side):
- NORMAL: Standard market conditions, no special filters active
- HIGH VOL: High volatility detected, position reduction and exit sensitivity increased
- EXTREME VOL: Extreme volatility confirmed, enhanced protective protocols engaged
- NEWS FILTER: Major economic event detected, 80% position reduction active
- GAP MODE: Weekend gap identified, increased caution until normal flow resumes
Combined Status Interpretation:
- NORMAL | NORMAL: Suitable trading conditions, standard strategy operation
- HIGH | HIGH VOL: Elevated volatility confirmed by both systems, 40% position reduction
- EXTREME | EXTREME VOL: High volatility warning, 70% position reduction active
📊VISUAL SYSTEM INTEGRATION
Chart Analysis & Market Visualization
CCI SMART Buffer Zone Visualization System - Dynamic Support/Resistance Framework
Dynamic Zone Architecture: The CCI SMART buffer system represents systematic visual integration creating adaptive support and resistance zones that automatically expand and contract based on current market volatility through ALMA-smoothed true range calculations. These dynamic zones provide real-time support and resistance levels that adapt to evolving market conditions rather than static horizontal lines that quickly become obsolete.
Adaptive Color Intensity Algorithm: The buffer visualization employs color intensity algorithms where transparency and saturation automatically adjust based on CCI momentum strength and directional persistence. Stronger momentum conditions produce more opaque visual representations with increased saturation, while weaker momentum creates subtle transparency indicating reduced prominence or significance.
Color Interpretation Framework for Strategic Decision Making:
-Intense Blue/Purple (High Opacity): Strong CCI readings exceeding ±80 with notable momentum strength indicating support/resistance zones suitable for increased position management decisions
• Moderate Blue/Purple (Medium Opacity): Standard CCI readings ranging ±40-80 with normal momentum indicating support/resistance areas for standard position management protocols
• Faded Blue/Purple (High Transparency): Weak CCI readings below ±40 with minimal momentum suggesting cautious interpretation and conservative position management approaches
• Dynamic Color Transitions: Automatic real-time shifts between bullish (blue spectrum) and bearish (purple spectrum) based on CCI trend direction and momentum persistence characteristics
CCI Inflection Circle System - Momentum Reversal Identification: The inflection detection system creates distinctive visual alerts through dual-circle design combining solid cores with transparent glow effects for enhanced visibility across different chart backgrounds and timeframe configurations.
Inflection Circle Classification:
• Neon Green Circles: CCI extreme bullish inflection detected (>80 threshold) with systematic core + glow effect indicating bearish reversal warning for position management evaluation
• Hot Pink Circles: CCI extreme bearish inflection detected (<-80 threshold) with dual-layer visualization indicating bullish reversal opportunity for strategic entry consideration
• Dual-Circle Design Architecture: Solid tiny core providing location identification with large transparent glow ensuring visibility without chart obstruction across multiple timeframe analyses
SAR Visual Network - Multi-Layer Momentum Display Architecture
SAR Visualization Framework: The SAR visual system implements structured multi-layer display architecture incorporating trend lines, strength classification markers, and momentum analysis through various visual elements that automatically adapt to current momentum conditions and strength characteristics.
SAR Strength Visual Classification System:
• Bright Triangles (High Intensity): Strong SAR momentum exceeding 75% of calculated strength range, indicating significant momentum quality suitable for increased positioning considerations and extended target scenarios
• Standard Circles (Medium Intensity): Moderate SAR momentum within 25-75% strength range, representing normal momentum development appropriate for standard positioning approaches and regular target expectations
• Faded Markers (Low Intensity): Weak SAR momentum below 25% strength range, suggesting caution and conservative positioning during minimal momentum conditions with increased exit sensitivity
⚠️IMPORTANT DISCLAIMERS AND RISK WARNINGS
Past Performance Limitations: The backtesting results presented represent hypothetical performance based on historical market data and do not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Users must approach trading with appropriate caution, never risking more than they can afford to lose.
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
SwingTrade VWAP Strategy[TiamatCrypto]V1.1This Pine Script® code creates a trading strategy called "SwingTrade VWAP Strategy V1.1." This strategy incorporates various trading tools, such as VWAP (Volume Weighted Average Price), ADX (Average Directional Index), and volume signals. Below is an explanation of the components and logic within the script:
### Overview of Features
- **VWAP:** A volume-weighted moving average that assesses price trends relative to the VWAP level.
- **ADX:** A trend strength indicator that helps confirm the strength of bullish or bearish trends.
- **Volume Analysis:** Leverages volume data to gauge momentum and identify volume-weighted buy/sell conditions.
- **Dynamic Entry/Exit Signals:** Combines the above indicators to produce actionable buy/sell or exit signals.
- **Customizable Inputs:** Inputs for tuning parameters like VWAP period, ADX thresholds, and volume sensitivity.
---
### **Code Breakdown**
#### **Input Parameters**
The script begins by defining several user-configurable variables under groups. These include indicators' on/off switches (`showVWAP`, `enableADX`, `enableVolume`) and input parameters for VWAP, ADX thresholds, and volume sensitivity:
- **VWAP Period and Threshold:** Controls sensitivity for VWAP signal generation.
- **ADX Settings:** Allows users to configure the ADX period and strength threshold.
- **Volume Ratio:** Detects bullish/bearish conditions based on relative volume patterns.
---
#### **VWAP Calculation**
The script calculates VWAP using the formula:
\
Where `P` is the typical price (`(high + low + close)/3`) and `V` is the volume.
- It resets cumulative values (`sumPV` and `sumV`) at the start of each day.
- Delta percentage (`deltaPercent`) is calculated as the percentage difference between the close price and the VWAP.
---
#### **Indicators and Signals**
1. **VWAP Trend Signals:**
- Identifies bullish/bearish conditions based on price movement (`aboveVWAP`, `belowVWAP`) and whether the price is crossing the VWAP level (`crossingUp`, `crossingDown`).
- Also detects rising/falling delta changes based on the VWAP threshold.
2. **ADX Calculation:**
- Calculates the directional movement (`PlusDM`, `MinusDM`) and smoothed values for `PlusDI`, `MinusDI`, and `ADX`.
- Confirms strong bullish/bearish trends when ADX crosses the defined threshold.
3. **Volume-Based Signals:**
- Evaluates the ratio of bullish volume (when `close > VWAP`) to bearish volume (when `close < VWAP`) over a specified lookback period.
---
#### **Trade Signals**
The buy and sell signals are determined by combining conditions from the VWAP, ADX, and volume signals:
- **Buy Signal:** Triggered when price upward crossover VWAP, delta rises above the threshold, ADX indicates a strong bullish trend, and volume confirms bullish momentum.
- **Sell Signal:** Triggered under inverse conditions.
- Additionally, exit conditions (`exitLong` and `exitShort`) are based on VWAP crossovers combined with the reversal of delta values.
---
#### **Plotting and Display**
The strategy plots VWAP on the chart and adds signal markers for:
- **Buy/Long Entry:** Green triangle below bars.
- **Sell/Short Entry:** Red triangle above bars.
- **Exit Signals:** Lime or orange "X" shapes for exits from long/short positions.
- Additionally, optional text labels are displayed to indicate the type of signal.
---
#### **Trading Logic**
The script's trading logic executes as follows:
- **Entries:**
- Executes long trades when the `buySignal` condition is true.
- Executes short trades when the `sellSignal` condition is true.
- **Exits:**
- Closes long positions upon `exitLong` conditions.
- Closes short positions upon `exitShort` conditions.
- The strategy calculates profits and visualizes the trade entry, exit, and running profit within the chart.
---
#### **Alerts**
Alerts are set up to notify traders via custom signals for buy and sell trades.
---
### **Use Case**
This script is suitable for day traders, swing traders, or algorithmic traders who rely on confluence signals from VWAP, ADX, and volume momentum. Its modular structure (e.g., the ability to enable/disable specific indicators) makes it highly customizable for various trading styles and financial instruments.
#### **Customizability**
- Adjust VWAP, ADX, and volume sensitivity levels to fit unique market conditions or asset classes.
- Turn off specific criteria to focus only on VWAP or ADX signals if desired.
#### **Caution**
As with all trading strategies, this script should be used for backtesting and analysis before live implementation. It's essential to validate its performance on historical data while considering factors like slippage and transaction costs.
RTB - Momentum Breakout Strategy V3
📈 RTB - Momentum Breakout Strategy V3 is a directional breakout strategy based on momentum. It combines exponential moving averages (EMAs), RSI, and recent support/resistance levels to detect breakout entries with trend confirmation. The system includes dynamic risk management using ATR-based stop-loss and trailing stop levels. Webhook alerts are supported for external automated trading integrations.
🔎 The strategy was backtested using default parameters on BTCUSDT Futures (Bybit) with 4-hour timeframe and a 0.05% commission per trade.
⚠️ This script is for educational purposes only and does not constitute financial advice. Always do your own research before trading.
ADX for BTC [PineIndicators]The ADX Strategy for BTC is a trend-following system that uses the Average Directional Index (ADX) to determine market strength and momentum shifts. Designed for Bitcoin trading, this strategy applies a customizable ADX threshold to confirm trend signals and optionally filters entries using a Simple Moving Average (SMA). The system features automated entry and exit conditions, dynamic trade visualization, and built-in trade tracking for historical performance analysis.
⚙️ Core Strategy Components
1️⃣ Average Directional Index (ADX) Calculation
The ADX indicator measures trend strength without indicating direction. It is derived from the Positive Directional Movement (+DI) and Negative Directional Movement (-DI):
+DI (Positive Directional Index): Measures upward price movement.
-DI (Negative Directional Index): Measures downward price movement.
ADX Value: Higher values indicate stronger trends, regardless of direction.
This strategy uses a default ADX length of 14 to smooth out short-term fluctuations while detecting sustainable trends.
2️⃣ SMA Filter (Optional Trend Confirmation)
The strategy includes a 200-period SMA filter to validate trend direction before entering trades. If enabled:
✅ Long Entry is only allowed when price is above a long-term SMA multiplier (5x the standard SMA length).
✅ If disabled, the strategy only considers the ADX crossover threshold for trade entries.
This filter helps reduce entries in sideways or weak-trend conditions, improving signal reliability.
📌 Trade Logic & Conditions
🔹 Long Entry Conditions
A buy signal is triggered when:
✅ ADX crosses above the threshold (default = 14), indicating a strengthening trend.
✅ (If SMA filter is enabled) Price is above the long-term SMA multiplier.
🔻 Exit Conditions
A position is closed when:
✅ ADX crosses below the stop threshold (default = 45), signaling trend weakening.
By adjusting the entry and exit ADX levels, traders can fine-tune sensitivity to trend changes.
📏 Trade Visualization & Tracking
Trade Markers
"Buy" label (▲) appears when a long position is opened.
"Close" label (▼) appears when a position is exited.
Trade History Boxes
Green if a trade is profitable.
Red if a trade closes at a loss.
Trend Tracking Lines
Horizontal lines mark entry and exit prices.
A filled trade box visually represents trade duration and profitability.
These elements provide clear visual insights into trade execution and performance.
⚡ How to Use This Strategy
1️⃣ Apply the script to a BTC chart in TradingView.
2️⃣ Adjust ADX entry/exit levels based on trend sensitivity.
3️⃣ Enable or disable the SMA filter for trend confirmation.
4️⃣ Backtest performance to analyze historical trade execution.
5️⃣ Monitor trade markers and history boxes for real-time trend insights.
This strategy is designed for trend traders looking to capture high-momentum market conditions while filtering out weak trends.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
Ultimate Stochastics Strategy by NHBprod Use to Day Trade BTCHey All!
Here's a new script I worked on that's super simple but at the same time useful. Check out the backtest results. The backtest results include slippage and fees/commission, and is still quite profitable. Obviously the profitability magnitude depends on how much capital you begin with, and how much the user utilizes per order, but in any event it seems to be profitable according to backtests.
This is different because it allows you full functionality over the stochastics calculations which is designed for random datasets. This script allows you to:
Designate ANY period of time to analyze and study
Choose between Long trading, short trading, and Long & Short trading
It allows you to enter trades based on the stochastics calculations
It allows you to EXIT trades using the stochastics calculations or take profit, or stop loss, Or any combination of those, which is nice because then the user can see how one variable effects the overall performance.
As for the actual stochastics formula, you get control, and get to SEE the plot lines for slow K, slow D, and fast K, which is usually not considered.
You also get the chance to modify the smoothing method, which has not been done with regular stochastics indicators. You get to choose the standard simple moving average (SMA) method, but I also allow you to choose other MA's such as the HMA and WMA.
Lastly, the user gets the option of using a custom trade extender, which essentially allows a buy or sell signal to exist for X amount of candles after the initial signal. For example, you can use "max bars since signal" to 1, and this will allow the indicator to produce an extra sequential buy signal when a buy signal is generated. This can be useful because it is possible that you use a small take profit (TP) and quickly exit a profitable trade. With the max bars since signal variable, you're able to reenter on the next candle and allow for another opportunity.
Let me know if you have any questions! Please take a look at the performance report and let me know your thoughts! :)
Crypto Volatility Bitcoin Correlation Strategy Description:
The Crypto Volatility Bitcoin Correlation Strategy is designed to leverage market volatility specifically in Bitcoin (BTC) using a combination of volatility indicators and trend-following techniques. This strategy utilizes the VIXFix (a volatility indicator adapted for crypto markets) and the BVOL7D (Bitcoin 7-Day Volatility Index from BitMEX) to identify periods of high volatility, while confirming trends with the Exponential Moving Average (EMA). These components work together to offer a comprehensive system that traders can use to enter positions when volatility and trends are aligned in their favor.
Key Features:
VIXFix (Volatility Index for Crypto Markets): This indicator measures the highest price of Bitcoin over a set period and compares it with the current low price to gauge market volatility. A rise in VIXFix indicates increasing market volatility, signaling that large price movements could occur.
BVOL7D (Bitcoin 7-Day Volatility Index): This volatility index, provided by BitMEX, measures the volatility of Bitcoin over the past 7 days. It helps traders monitor the recent volatility trend in the market, particularly useful when making short-term trading decisions.
Exponential Moving Average (EMA): The 50-period EMA acts as a trend indicator. When the price is above the EMA, it suggests the market is in an uptrend, and when the price is below the EMA, it suggests a downtrend.
How It Works:
Long Entry: A long position is triggered when both the VIXFix and BVOL7D indicators are rising, signaling increased volatility, and the price is above the 50-period EMA, confirming that the market is trending upward.
Exit: The strategy exits the position when the price crosses below the 50-period EMA, which signals a potential weakening of the uptrend and a decrease in volatility.
This strategy ensures that traders only enter positions when the volatility aligns with a clear trend, minimizing the risk of entering trades during periods of market uncertainty.
Testing and Timeframe:
This strategy has been tested on Bitcoin using the daily timeframe, which provides a longer-term perspective on market trends and volatility. However, users can adjust the timeframe according to their trading preferences. It is crucial to note that this strategy does not include comprehensive risk management, aside from the exit condition when the price crosses below the EMA. Users are strongly advised to implement their own risk management techniques, such as setting appropriate stop-loss levels, to safeguard their positions during high volatility periods.
Utility:
The Crypto Volatility Bitcoin Correlation Strategy is particularly well-suited for traders who aim to capitalize on the high volatility often seen in the Bitcoin market. By combining volatility measurements (VIXFix and BVOL7D) with a trend-following mechanism (EMA), this strategy helps identify optimal moments for entering and exiting trades. This approach ensures that traders participate in potentially profitable market moves while minimizing exposure during times of uncertainty.
Use Cases:
Volatility-Based Entries: Traders looking to take advantage of market volatility spikes will find this strategy useful for timing entry points during market swings.
Trend Confirmation: By using the EMA as a confirmation tool, traders can avoid entering trades that go against the trend, which can result in significant losses during volatile market conditions.
Risk Management: While the strategy exits when price falls below the EMA, it is important to recognize that this is not a full risk management system. Traders should use caution and integrate additional risk measures, such as stop-losses and position sizing, to better manage potential losses.
How to Use:
Step 1: Monitor the VIXFix and BVOL7D indicators. When both are rising and the Bitcoin price is above the EMA, the strategy will trigger a long entry, indicating that the market is experiencing increased volatility with a confirmed uptrend.
Step 2: Exit the position when the price drops below the 50-period EMA, signaling that the trend may be reversing or weakening, reducing the likelihood of continued upward price movement.
This strategy is open-source and is intended to help traders navigate volatile market conditions, particularly in Bitcoin, using proven indicators for volatility and trend confirmation.
Risk Disclaimer:
This strategy has been tested on the daily timeframe of Bitcoin, but users should be aware that it does not include built-in risk management except for the below-EMA exit condition. Users should be extremely cautious when using this strategy and are encouraged to implement their own risk management, such as using stop-losses, position sizing, and setting appropriate limits. Trading involves significant risk, and this strategy does not guarantee profits or prevent losses. Past performance is not indicative of future results. Always test any strategy in a demo environment before applying it to live markets.
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
ETH Signal 15m
This strategy uses the Supertrend indicator combined with RSI to generate buy and sell signals, with stop loss (SL) and take profit (TP) conditions based on ATR (Average True Range). Below is a detailed explanation of each part:
1. General Information BINANCE:ETHUSDT.P
Strategy Name: "ETH Signal 15m"
Designed for use on the 15-minute time frame for the ETH pair.
Default capital allocation is 15% of total equity for each trade.
2. Backtest Period
start_time and end_time: Define the start and end time of the backtest period.
start_time = 2024-08-01: Start date of the backtest.
end_time = 2054-01-01: End date of the backtest.
The strategy will only run when the current time falls within this specified range.
3. Supertrend Indicator
Supertrend is a trend-following indicator that provides buy or sell signals based on the direction of price changes.
factor = 2.76: The multiplier used in the Supertrend calculation (increasing this value makes the Supertrend less sensitive to price movements).
atrPeriod = 12: Number of periods used to calculate ATR.
Output:
direction: Determines the buy/sell direction based on Supertrend.
If direction decreases, it signals a buy (Long).
If direction increases, it signals a sell (Short).
4. RSI Indicator
RSI (Relative Strength Index) is a momentum indicator, often used to identify overbought or oversold conditions.
rsiLength = 12: Number of periods used to calculate RSI.
rsiOverbought = 70: RSI level considered overbought.
rsiOversold = 30: RSI level considered oversold.
5. Entry Conditions
Long Entry:
Supertrend gives a buy signal (ta.change(direction) < 0).
RSI must be below the overbought level (rsi < rsiOverbought).
Short Entry:
Supertrend gives a sell signal (ta.change(direction) > 0).
RSI must be above the oversold level (rsi > rsiOversold).
The strategy will only execute trades if the current time is within the backtest period (in_date_range).
6. Stop Loss (SL) and Take Profit (TP) Conditions
ATR (Average True Range) is used to calculate the distance for Stop Loss and Take Profit based on price volatility.
atr = ta.atr(atrPeriod): ATR is calculated using 12 periods.
Stop Loss and Take Profit are calculated as follows:
Long Trade:
Stop Loss: Set at close - 4 * atr (current price minus 4 times the ATR).
Take Profit: Set at close + 2 * atr (current price plus 2 times the ATR).
Short Trade:
Stop Loss: Set at close + 4 * atr (current price plus 4 times the ATR).
Take Profit: Set at close - 2.237 * atr (current price minus 2.237 times the ATR).
Summary:
This strategy enters a Long trade when the Supertrend indicates an upward trend and RSI is not in the overbought region. Conversely, a Short trade is entered when Supertrend signals a downtrend, and RSI is not oversold.
The trade is exited when the price reaches the Stop Loss or Take Profit levels, which are determined based on price volatility (ATR).
Disclaimer:
The content provided in this strategy is for informational and educational purposes only. It is not intended as financial, investment, or trading advice. Trading in cryptocurrency, stocks, or any financial markets involves significant risk, and you may lose more than your initial investment. Past performance is not indicative of future results, and no guarantee of profit can be made. You should consult with a professional financial advisor before making any investment decisions. The creator of this strategy is not responsible for any financial losses or damages incurred as a result of following this strategy. All trades are executed at your own risk.
Rsi Long-Term Strategy [15min]Hello, I would like to present to you The "RSI Long-Term Strategy" for 15min tf
The "RSI Long-Term Strategy " is designed for traders who prefer a combination of momentum and trend-following techniques. The strategy focuses on entering long positions during significant market corrections within an overall uptrend, confirmed by both RSI and volume. The use of long-term SMAs ensures that trades are made in line with the broader market trend. The stop-loss feature provides risk management by limiting losses on trades that do not perform as expected. This strategy is particularly well-suited for longer-term traders who monitor 15-minute charts but look for substantial trend reversals or continuations.
Indicators and Parameters:
Relative Strength Index (RSI):
- The RSI is calculated using a 10-period length. It measures the magnitude of recent price changes to evaluate overbought or oversold conditions. The script defines oversold conditions when the RSI is at or below 30 and overbought conditions when the RSI is at or above 70.
Volume Condition:
-The strategy incorporates a volume condition where the current volume must be greater than 2.5 times the 20-period moving average of volume. This is used to confirm the strength of the price movement.
Simple Moving Averages (SMA):
- The strategy uses two SMAs: SMA1 with a length of 250 periods and SMA2 with a length of 500 periods. These SMAs help identify long-term trends and generate signals based on their crossover.
Strategy Logic:
Entry Logic:
A long position is initiated when all the following conditions are met:
The RSI indicates an oversold condition (RSI ≤ 30).
SMA1 is above SMA2, indicating an uptrend.
The volume condition is satisfied, confirming the strength of the signal.
Exit Logic:
The strategy closes the long position when SMA1 crosses under SMA2, signaling a potential end of the uptrend (a "Death Cross").
Stop-Loss:
A stop-loss is set at 5% below the entry price to manage risk and limit potential losses.
Buy and sell signals are highlighted with circles below or above bars:
Green Circle : Buy signal when RSI is oversold, SMA1 > SMA2, and the volume condition is met.
Red Circle : Sell signal when RSI is overbought, SMA1 < SMA2, and the volume condition is met.
Black Cross: "Death Cross" when SMA1 crosses under SMA2, indicating a potential bearish signal.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
Project Monday Strategy [AlgoAI System]Overview
Project Monday is a sophisticated trading strategy designed for active market participants. This strategy can be used alongside other forms of technical analysis, providing traders with additional tools to enhance their market insights. While it offers a flexible approach for identifying and exploiting market inefficiencies, Project Monday does not fit every market condition and requires adjustments. Its core principles include technical analysis and risk management, all aimed at making informed trading decisions and managing risk effectively.
Features
Project Monday Strategy works in any market and includes many features:
Efficient Trading Presets: Offers ready-to-use presets that allow traders to start efficient trading with one click.
Confirmation Signals: Provides signals to help traders validate trends, emphasizing informed decision-making (not to be followed blindly).
Reversal Signals: Identifies signals to alert traders to potential reversals, encouraging careful analysis (not to be followed blindly).
Adaptability: Can be adjusted to fit different market conditions, ensuring ongoing effectiveness.
Multi-Market Application: Suitable for use across various asset classes including stocks, forex, commodities, and cryptocurrencies.
Integration: Can be used alongside other technical analysis tools for enhanced decision-making.
Position Sizing: Allows traders to determine optimal trade size using backtesting and trading performance dashboard.
Backtesting: Supports historical testing to refine and validate the strategy.
Continuous Monitoring: Includes features for ongoing performance evaluation and strategy adjustments.
Unique Project Monday Strategy Features on TradingView:
Adaptive Position Sizing: Dynamically adjusts the size of each position based on market conditions and predefined risk management criteria, ensuring optimal trade sizing and risk exposure.
Preliminary Position Opening: Allows traders to enter a position in anticipation of a signal confirmation, enabling them to capture early market movements and improve entry points.
Preliminary Position Closing: Enables traders to exit a position before a signal reversal, helping to lock in profits and minimize potential losses during volatile market conditions.
Adjusting Strategy Parameters:
Price Band Inputs:
Project Monday Strategy uses a set of configurable inputs to tailor its behavior according to the trader's preferences. The following are the key inputs for the price band calculations. Signals are not generated when the price remains within these bands.
“Length of Calculation” determines how many historical data points are used in the trend calculation. A shorter “Length of Calculation” will make the Price Band more responsive to recent price changes but may also increase the noise and the likelihood of false signals. A longer “Length of Calculation” will make the Price Band smoother, with less noise, but may cause more lag in reacting to price changes.
“Offset” determines the position of the Gaussian filter, which is used to weight the data points in the trend calculation. The offset is expressed as a fraction of the “Length of Calculation”, with a value between 0 and 1. A higher “Offset” will shift the Gaussian filter closer to the more recent data points, making the Price Band more responsive to recent price changes but potentially increasing noise. A lower “Offset” will shift the Gaussian filter closer to the centre of the window, resulting in a smoother Price Band but potentially introducing more lag.
“Sigma” refers to the standard deviation used in the Gaussian distribution function. This parameter determines the smoothness of the curve and the degree to which data points close to the centre of the “Length of Calculation” are weighted more heavily than those further away. A smaller “Sigma” will result in a narrower Gaussian filter, leading to a more responsive Price Band but with a higher chance of noise and false signals. A larger “Sigma” will result in a wider Gaussian filter, creating a smoother Price Band but with more lag.
Adjust the “Source” inputs to specify which type of price data should be used for strategy calculations and signal generation.
“Width of Band” input determines the multiplier for the band width. A higher value of “Width of Band” makes the price band wider, which generates fewer signals due to the lower probability of the price moving outside the band. Conversely, a lower multiplier makes the band narrower, generating more signals but also increasing the likelihood of false signals.
Direction input:
The Project Monday strategy includes an input to specify the direction of trades, allowing traders to control whether the strategy should consider long positions, short positions, or both. The following input parameter is used for this purpose:
This input parameter allows traders to define the type of positions the strategy will take. It has three options:
Only Long: The strategy will generate signals exclusively for buying or closing short positions, focusing on potential uptrends.
Only Short: The strategy will generate signals exclusively for selling or closing long positions, focusing on potential downtrends.
Both: The strategy will generate signals for both buying (long positions) and selling (short positions), allowing for a more comprehensive trading approach that captures opportunities in both rising and falling markets.
Signals Filter:
The Project Monday strategy includes inputs to filter signals based on higher timeframes and the length of the data used for filtering. These inputs help traders refine the strategy's performance by considering broader market trends and smoothing out short-term fluctuations.
Filter Timeframe input specifies the timeframe used for filtering signals. By choosing a higher timeframe, traders can filter out noise from shorter timeframes and focus on more significant trends. The options range from intraday minutes (e.g., 1, 5, 15 minutes) to daily (1D, 2D, etc.), weekly (1W, 2W, etc.), and monthly (1M) timeframes. This allows traders to align their strategy with their preferred trading horizon and market perspective.
Filter Length input defines the number of data points used for filtering signals on the selected timeframe. A longer filter length will smooth out the data more, helping to identify sustained trends and reduce the impact of short-term fluctuations. Conversely, a shorter filter length will make the filter more responsive to recent price changes, potentially generating more signals but also increasing sensitivity to market noise.
Adaptive Position Size:
The Project Monday strategy incorporates inputs for unique feature Adaptive Position Sizing (APS), which dynamically adjusts the size of trades based on market conditions and specified parameters. This feature helps optimize risk management and trading performance.
Enable Adaptive Position Size: Users can check or uncheck this box to enable or disable the Adaptive Position Size feature. When checked, the strategy dynamically adjusts position sizes based on the defined parameters. This allows traders to scale their positions according to market volatility and other factors, enhancing risk management and potentially improving returns. When unchecked, the strategy will not adjust position sizes adaptively, and positions will remain fixed as per other settings.
“Timeframe for Adaptive Position Size “input specifies the timeframe used for calculating the position size. Options range from intraday minutes (e.g., 30, 60 minutes) to daily (1D, 3D), weekly (1W), and monthly (1M) timeframes. Selecting an appropriate timeframe helps align position sizing calculations with the trader’s overall strategy and market perspective, ensuring that position sizes are adjusted based on relevant market data.
“APS Length” input defines the number of data points used to calculate the adaptive position size. A longer APS length will result in higher position sizes. Conversely, a shorter APS length will result in smaller position sizes.
Anticipatory Trading:
Project Monday Strategy includes inputs for unique feature Anticipatory Trading, allowing traders to open and close positions preliminarily based on certain conditions. This feature aims to provide an edge by taking action before traditional signals confirm.
Enable Preliminary Position Opening: Users can check or uncheck this box to enable or disable Preliminary Position Opening. When enabled, the strategy will open positions based on preliminary conditions before the standard signals are confirmed. This can help traders capitalize on early trend movements and potentially gain a better entry point.
Enable Preliminary Position Closing: Users can check or uncheck this box to enable or disable Preliminary Position Closing. When enabled, the strategy will close positions based on preliminary conditions before the standard exit signals are confirmed. This can help traders lock in profits or limit losses by exiting positions at the early signs of trend reversals.
“Position Size in %” input specifies the position size as a percentage of the trading capital. By setting this value, traders can control the amount of capital allocated to each trade. For example, a risk value of 40% means that 40% of the available trading capital will be used for each anticipatory trade. This helps in managing risk and ensuring that the position size aligns with the trader's risk tolerance and overall strategy.
Usage:
Signal Generation
Long signal indicates a potential uptrend, suggesting either buying or closing a short position. Short signal indicates a potential downtrend, suggesting either selling or closing a long position. Signals are generated on your chart when the price moves beyond a calculated price band based on the current trend.
Signal Filtering
The strategy includes a filtering mechanism based on the current or another timeframe. Filtering works best with higher timeframes. This component calculates the trend on a higher timeframe and predicts the trend, ensuring trades on the current timeframe are only opened if they align with the higher timeframe trend. Setting the right filter timeframe is crucial for obtaining the best signals.
Position Direction
Users can choose the direction of positions to open via the settings box. Options include only long positions, only short positions, or both.
Adaptive Position Size (APS)
Users can enable the Adaptive Position Size feature to adjust position sizes based on trend strength. The strategy evaluates the strength of the current trend based on a higher timeframe. The stronger the trend, the larger the position size for opening a position.
Anticipatory Trading
Users can activate this unique feature to enhance trading decisions. The strategy assesses the likelihood of receiving a main signal. If the opportunity appears strong, it opens a partial position, as specified in the settings box. As the probability of the signal strengthens, the strategy gradually increases the position size.
Exit Strategy
The strategy exits positions based on receiving a reverse signal. Positions opened through “Anticipatory trading” are exited incrementally as each preliminary signal reverses.
By following these steps, traders can implement the strategy to navigate various market scenarios, manage risk, and adjust trading performance over time. Adjusting parameters and monitoring signals diligently are key to adapting the strategy to individual trading styles and market conditions.
You will get
By purchasing the Project Monday strategy, you not only gain access to a cutting-edge system but also receive ready-to-use presets designed to help you start trading immediately and achieve optimal results. Additionally, you benefit from comprehensive support and the option to request custom presets for your desired financial instruments through our dedicated support team, ensuring you have the tools and assistance needed for successful trading.
Risk Disclaimer
This information is not a personalized investment recommendation, and the financial instruments or transactions mentioned in it may not be appropriate for your financial situation, investment objective(s), risk tolerance, and/or expected return. AlgoAI shall not be liable for any losses incurred in the event of transactions or investments in financial instruments mentioned in this information.
Intelle_city - World Cycle - Ath & Atl - Logarithmic - Strategy.Overview
Indicators: Strategy !
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Strategy - The Pi Cycle Top and Bottom Oscillator is an adaptation of the original Pi Cycle Top chart. It compares the 111-Day Moving Average circle and the 2 * 350-Day Moving Average circle of Bitcoin’s Price. These two moving averages were selected as 350 / 111 = 3.153; An approximation of the important mathematical number Pi.
When the 111-Day Moving Average circle reaches the 2 * 350-Day Moving Average circle, it indicates that the market is becoming overheated. That is because the mid time frame momentum reference of the 111-Day Moving Average has caught up with the long timeframe momentum reference of the 2 * 350-Day Moving Average.
Historically this has occurred within 3 days of the very top of each market cycle.
When the 111 Day Moving Average circle falls back beneath the 2 * 350 Day Moving Average circle, it indicates that the market momentum of that cycle is significantly cooling down. The oscillator drops down into the lower green band shown where the 111 Day Moving Average is moving at a 75% discount relative to the 2 * 350 Day Moving Average.
Historically, this has highlighted broad areas of bear market lows.
IMPORTANT: You need to set a LOGARITHMIC graph. (The function is located at the bottom right of the screen)
IMPORTANT: The INTELLECT_city indicator is made for a buy-sell strategy; there is also a signal indicator from INTELLECT_city
IMPORTANT: The Chart shows all cycles, both buying and selling.
IMPORTANT: Suitable timeframes are 1 daily (recommended) and 1 weekly
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Описание на русском:
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Обзор индикатора
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Strategy - Логарифмический - Сигнал - Осциллятор вершины и основания цикла Пи представляет собой адаптацию оригинального графика вершины цикла Пи. Он сравнивает круг 111-дневной скользящей средней и круг 2 * 350-дневной скользящей средней цены Биткойна. Эти две скользящие средние были выбраны как 350/111 = 3,153; Приближение важного математического числа Пи.
Когда круг 111-дневной скользящей средней достигает круга 2 * 350-дневной скользящей средней, это указывает на то, что рынок перегревается. Это происходит потому, что опорный моментум среднего временного интервала 111-дневной скользящей средней догнал опорный момент импульса длинного таймфрейма 2 * 350-дневной скользящей средней.
Исторически это происходило в течение трех дней после вершины каждого рыночного цикла.
Когда круг 111-дневной скользящей средней опускается ниже круга 2 * 350-дневной скользящей средней, это указывает на то, что рыночный импульс этого цикла значительно снижается. Осциллятор опускается в нижнюю зеленую полосу, показанную там, где 111-дневная скользящая средняя движется со скидкой 75% относительно 2 * 350-дневной скользящей средней.
Исторически это высветило широкие области минимумов медвежьего рынка.
ВАЖНО: Выставлять нужно ЛОГАРИФМИЧЕСКИЙ график. (Находиться функция с правой нижней части экрана)
ВАЖНО: Индикатор INTELLECT_city сделан для стратегии покупок продаж, есть также и сигнальный от INTELLECT_сity
ВАЖНО: На Графике видны все циклы, как на покупку так и на продажу.
ВАЖНО: Подходящие таймфреймы 1 дневной (рекомендовано) и 1 недельный
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Beschreibung - Deutsch
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Indikatorübersicht
INTELLECT_city – Weltzyklus – ATH & ATL – Zeitrahmen 1T und 1W – Logarithmisch – Strategy – Der Pi-Zyklus-Top- und Bottom-Oszillator ist eine Anpassung des ursprünglichen Pi-Zyklus-Top-Diagramms. Er vergleicht den 111-Tage-Gleitenden-Durchschnittskreis und den 2 * 350-Tage-Gleitenden-Durchschnittskreis des Bitcoin-Preises. Diese beiden gleitenden Durchschnitte wurden als 350 / 111 = 3,153 ausgewählt; eine Annäherung an die wichtige mathematische Zahl Pi.
Wenn der 111-Tage-Gleitenden-Durchschnittskreis den 2 * 350-Tage-Gleitenden-Durchschnittskreis erreicht, deutet dies darauf hin, dass der Markt überhitzt. Das liegt daran, dass der Momentum-Referenzwert des 111-Tage-Gleitenden-Durchschnitts im mittleren Zeitrahmen den Momentum-Referenzwert des 2 * 350-Tage-Gleitenden-Durchschnitts im langen Zeitrahmen eingeholt hat.
Historisch gesehen geschah dies innerhalb von 3 Tagen nach dem Höhepunkt jedes Marktzyklus.
Wenn der Kreis des 111-Tage-Durchschnitts wieder unter den Kreis des 2 x 350-Tage-Durchschnitts fällt, deutet dies darauf hin, dass die Marktdynamik dieses Zyklus deutlich nachlässt. Der Oszillator fällt in das untere grüne Band, in dem der 111-Tage-Durchschnitt mit einem Abschlag von 75 % gegenüber dem 2 x 350-Tage-Durchschnitt verläuft.
Historisch hat dies breite Bereiche mit Tiefstständen in der Baisse hervorgehoben.
WICHTIG: Sie müssen ein logarithmisches Diagramm festlegen. (Die Funktion befindet sich unten rechts auf dem Bildschirm)
WICHTIG: Der INTELLECT_city-Indikator ist für eine Kauf-Verkaufs-Strategie konzipiert; es gibt auch einen Signalindikator von INTELLECT_city
WICHTIG: Das Diagramm zeigt alle Zyklen, sowohl Kauf- als auch Verkaufszyklen.
WICHTIG: Geeignete Zeitrahmen sind 1 täglich (empfohlen) und 1 wöchentlich
Bitcoin Futures vs. Spot Tri-Frame - Strategy [presentTrading]Prove idea with a backtest is always true for trading.
I developed and open-sourced it as an educational material for crypto traders to understand that the futures and spot spread may be effective but not be as effective as they might think. It serves as an indicator of sentiment rather than a reliable predictor of market trends over certain periods. It is better suited for specific trading environments, which require further research.
█ Introduction and How it is Different
The "Bitcoin Futures vs. Spot Tri-Frame Strategy" utilizes three different timeframes to calculate the Z-Score of the spread between BTC futures and spot prices on Binance and OKX exchanges. The strategy executes long or short trades based on composite Z-Score conditions across the three timeframes.
The spread refers to the difference in price between BTC futures and BTC spot prices, calculated by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges.
BTCUSD 1D L/S Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Spread
The spread is the difference in price between BTC futures and BTC spot prices. The strategy calculates the spread by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges. This spread serves as the primary metric for identifying trading opportunities.
Spread = Weighted Average Futures Price - Weighted Average Spot Price
🔶 Z-Score Calculation
The Z-Score measures how many standard deviations the current spread is from its historical mean. This is calculated for each timeframe as follows:
Spread Mean_tf = SMA(Spread_tf, longTermSMA)
Spread StdDev_tf = STDEV(Spread_tf, longTermSMA)
Z-Score_tf = (Spread_tf - Spread Mean_tf) / Spread StdDev_tf
Local performance
🔶 Composite Entry Conditions
The strategy triggers long and short entries based on composite Z-Score conditions across all three timeframes:
- Long Condition: All three Z-Scores must be greater than the long entry threshold.
Long Condition = (Z-Score_tf1 > zScoreLongEntryThreshold) and (Z-Score_tf2 > zScoreLongEntryThreshold) and (Z-Score_tf3 > zScoreLongEntryThreshold)
- Short Condition: All three Z-Scores must be less than the short entry threshold.
Short Condition = (Z-Score_tf1 < zScoreShortEntryThreshold) and (Z-Score_tf2 < zScoreShortEntryThreshold) and (Z-Score_tf3 < zScoreShortEntryThreshold)
█ Trade Direction
The strategy allows the user to specify the trading direction:
- Long: Only long trades are executed.
- Short: Only short trades are executed.
- Both: Both long and short trades are executed based on the Z-Score conditions.
█ Usage
The strategy can be applied to BTC or Crypto trading on major exchanges like Binance and OKX. By leveraging discrepancies between futures and spot prices, traders can exploit market inefficiencies. This strategy is suitable for traders who prefer a statistical approach and want to diversify their timeframes to validate signals.
█ Default Settings
- Input TF 1 (60 minutes): Sets the first timeframe for Z-Score calculation.
- Input TF 2 (120 minutes): Sets the second timeframe for Z-Score calculation.
- Input TF 3 (180 minutes): Sets the third timeframe for Z-Score calculation.
- Long Entry Z-Score Threshold (3): Defines the threshold above which a long trade is triggered.
- Short Entry Z-Score Threshold (-3): Defines the threshold below which a short trade is triggered.
- Long-Term SMA Period (100): The period used to calculate the simple moving average for the spread.
- Use Hold Days (true): Enables holding trades for a specified number of days.
- Hold Days (5): Number of days to hold the trade before exiting.
- TPSL Condition (None): Defines the conditions for taking profit and stop loss.
- Take Profit (%) (30.0): The percentage at which the trade will take profit.
- Stop Loss (%) (20.0): The percentage at which the trade will stop loss.
By fine-tuning these settings, traders can optimize the strategy to suit their risk tolerance and trading style, enhancing overall performance.
Calculus Free Trend Strategy for Crypto & StocksObjective :
The Correlation Channel Trading Strategy is designed to identify potential entry points based on the relationship between price movements and a correlation channel. The strategy aims to capture trends within the channel while managing risk effectively.
Parameters :
Length: Determines the period for calculating moving averages and the true range, influencing the sensitivity of the strategy to price movements.
Multiplier: Adjusts the width of the correlation channel, providing flexibility to adapt to different market conditions.
Inputs :
Asset Symbol: Allows users to specify the financial instrument for analysis.
Timeframe: Defines the timeframe for data aggregation, enabling customization based on trading preferences.
Plot Correlation Channel: Optional input to visualize the correlation channel on the price chart.
Methodology :
Data Acquisition: The strategy fetches OHLC (Open, High, Low, Close) data for the specified asset and timeframe. In this case we use COINBASE:BTCUSD
Calculation of Correlation Channel: It computes the squared values for OHLC data, calculates the average value (x), and then calculates the square root of x to derive the source value. Additionally, it calculates the True Range as the difference between high and low prices.
Moving Averages: The strategy calculates moving averages (MA) for the source value and the True Range, which form the basis for defining the correlation channel.
Upper and Lower Bands: Using the MA and True Range, the strategy computes upper and lower bands of the correlation channel, with the width determined by the multiplier.
Entry Conditions: Long positions are initiated when the price crosses above the upper band, signaling potential overbought conditions. Short positions are initiated when the price crosses below the lower band, indicating potential oversold conditions.
Exit Conditions: Stop-loss mechanisms are incorporated directly into the entry conditions to manage risk. Long positions are exited if the price falls below a predefined stop-loss level, while short positions are exited if the price rises above the stop-loss level.
Strategy Approach: The strategy aims to capitalize on trends within the correlation channel, leveraging systematic entry signals while actively managing risk through stop-loss orders.
Backtest Details : For the purpose of this test I used the entire data available for BTCUSD Coinbase, with 10% of capital allocation and 0.1% comission for entry/exit(0.2% total). Can be also used with other both directly correlated with current settings of BTC or with new ones
Advantages :
Provides a systematic approach to trading based on quantifiable criteria.
Offers flexibility through customizable parameters to adapt to various market conditions.
Integrates risk management through predefined stop-loss mechanisms.
Limitations :
Relies on historical price data and technical indicators, which may not always accurately predict future price movements.
May generate false signals during periods of low volatility or erratic price behavior.
Requires continuous monitoring and adjustment of parameters to maintain effectiveness.
Conclusion :
The Correlation Channel Trading Strategy offers traders a structured framework for identifying potential entry points within a defined price channel. By leveraging moving averages and true range calculations, the strategy aims to capture trends while minimizing risk through stop-loss mechanisms. While no strategy can guarantee success in all market conditions, the Correlation Channel Trading Strategy provides a systematic approach to trading that can enhance decision-making and risk management for traders.
