SMA Cross 5/50 with Trend Filter & Risk Management by JuggiDThe basic SMA (5/50) crossover strategy can be enhanced to improve profitability by adding filters and risk management. For example, a long entry is triggered only when the fast SMA (5) crosses above the slow SMA (50) **and** the price is above the SMA (200), ensuring trades align with the major trend. Similarly, a short entry requires the crossover confirmation plus the price staying below the SMA (200). To reduce false signals and protect capital, stop-loss and take-profit levels can be set automatically (e.g., 2% loss, 5% gain), while additional confirmation tools such as volume spikes, RSI above 50, or MACD momentum can be applied to validate stronger signals. This approach helps avoid whipsaws in sideways markets and allows trades to capture larger moves while minimizing downside risk.
Поиск скриптов по запросу "take profit"
Triple Quad Frosty v4.5Triple Quad Frosty v4.5 is a Renko-friendly strategy that lets you trade from up to four signal sources per side. Orders are only placed when your chosen conditional filters (A/B/C) agree, giving you full control over when entries are valid. You decide how signals must line up — from simple single-source triggers to majority or full agreement across all four.
Renko-based, with customizable static stops, take profits, and trailing stops. Time/day filters, daily trade limits, and forced closures let you restrict trading to specific windows.
The HTF filters in Triple Quad Frosty v4.5 use a higher-timeframe Hull Moving Average (HMA) to confirm trend direction, while slope and distance settings on the local HMA help filter out weak or choppy setups. Longs only trigger when price is above the HTF HMA and meets slope/distance requirements, and shorts only when the opposite is true.
Color-coded labels mark each exit as a win or loss, with reversal trades labeled separately for clarity. Conditional bars plotted above and below the chart show when the A/B/C filters align on a long or short bias, giving clear visual confirmation of entry conditions. Stop loss and take profit levels are plotted directly on the chart with guide lines, so you can easily track active trade management in real time.
Gann Trading Strategy📈 Simple & Powerful Gann-Based Trading System
This indicator automatically calculates key Gann support and resistance levels based on W.D. Gann's mathematical principles. Simply input either the highest price (for bearish setups) or lowest price (for bullish setups), and the indicator automatically generates all trading levels.
🎯 Key Features:
Automatic Level Calculation - Just select High/Low price projection basis
Complete Trading Framework - Entry zone, 3 Take Profit levels, and Exit level
Visual Trading Zones - Color-coded zones around each level for better entry/exit timing
Smart Alerts - Get notified when price reaches key levels
Bullish & Bearish Setups - Works for both long and short trading strategies
Customizable Display - Show/hide levels, adjust colors, line styles, and zone widths
🚀 How to Use:
Select "Lowest Price" for bullish setup or "Highest Price" for bearish setup
Input the relevant high or low price from your analysis
The indicator automatically calculates and displays all trading levels
Use the green entry zone for entries, blue levels for take profits, red level for exits
📊 Perfect for:
Swing trading
Position sizing and risk management
Support/resistance analysis
Multi-timeframe analysis
Uses time-tested Gann principles to automatically find key price levels for trading.
Wickless Heikin Ashi B/S [CHE]Wickless Heikin Ashi B/S \
Purpose.
Wickless Heikin Ashi B/S \ is built to surface only the cleanest momentum turns: it prints a Buy (B) when a bullish Heikin-Ashi candle forms with virtually no lower wick, and a Sell (S) when a bearish Heikin-Ashi candle forms with no upper wick. Optional Lock mode turns these into one-shot signals that hold the regime (bull or bear) until the opposite side appears. The tool can also project dashed horizontal lines from each signal’s price level to help you manage entries, stops, and partial take-profits visually.
How it works.
The indicator computes standard Heikin-Ashi values from your chart’s OHLC. A bar qualifies as bullish if its HA close is at or above its HA open; bearish if below. Then the wick on the relevant side is compared to the bar’s HA range. If that wick is smaller than your selected percentage threshold (plus a tiny tick epsilon to avoid rounding noise), the raw condition is considered “wickless.” Only one side can fire; on the rare occasion both raw conditions would overlap, the bar is ignored to prevent false dual triggers. When Lock is enabled, the first valid signal sets the active regime (background shaded light green for bull, light red for bear) and suppresses further same-side triggers until the opposite side appears, which helps reduce overtrading in chop.
Why wickless?
A missing wick on the “wrong” side of a Heikin-Ashi candle is a strong hint of persistent directional pressure. In practice, this filters out hesitation bars and many mid-bar flips. Traders who prefer entering only when momentum is decisive will find wickless bars useful for timing entries within an established bias.
Visuals you get.
When a valid buy appears, a small triangle “B” is plotted below the bar and a green dashed line can extend to the right from the signal’s HA open price. For sells, a triangle “S” above the bar and a red dashed line do the same. These lines act like immediate, price-anchored references for stop placement and profit scaling; you can shift the anchor left by a chosen number of bars if you prefer the line to start a little earlier for visual alignment.
How to trade it
Establish context first.
Pick a timeframe that matches your style: intraday index or crypto traders often use 5–60 minutes; swing traders might prefer 2–4 hours or daily. The tool is agnostic, but the cleanest results occur when the market is already trending or attempting a fresh breakout.
Entry.
When a B prints, the simplest rule is to enter long at or just after bar close. A conservative variation is to require price to take out the high of the signal bar in the next bar(s). For S, invert the logic: enter short on or after close, or only if price breaks the signal bar’s low.
Stop-loss.
Place the stop beyond the opposite extreme of the signal HA bar (for B: under the HA low; for S: above the HA high). If you prefer a static reference, use the dashed line level (signal HA open) or an ATR buffer (e.g., 1.0–1.5× ATR(14)). The goal is to give the trade enough room that normal noise does not immediately knock you out, while staying small enough to keep the risk contained.
Take-profit and management.
Two pragmatic approaches work well:
R-multiple scaling. Define your initial risk (distance from entry to stop). Scale out at 1R, 2R, and let a runner go toward 3R+ if structure holds.
Trailing logic. Trail behind a short moving average (e.g., EMA 20) or progressive swing points. Many traders also exit on the opposite signal when Lock flips, especially on faster timeframes.
Position sizing.
Keep risk per trade modest and consistent (e.g., 0.25–1% of account). The indicator improves timing; it does not replace risk control.
Settings guidance
Max lower wick for Bull (%) / Max upper wick for Bear (%).
These control how strict “wickless” must be. Tighter values (0.3–1.0%) yield fewer but cleaner signals and are great for strong trends or low-noise instruments. Looser values (1.5–3.0%) catch more setups in volatile markets but admit more noise. If you notice too many borderline bars triggering during high-volatility sessions, increase these thresholds slightly.
Lock (one-shot until opposite).
Keep Lock ON when you want one decisive signal per leg, reducing noise and signal clusters. Turn it OFF only if your plan intentionally scales into trends with multiple entries.
Extended lines & anchor offset.
Leave lines ON to maintain a visual memory of the last trigger levels. These often behave like near-term support/resistance. The offset simply lets you start that line one or more bars earlier if you prefer the look; it does not change the math.
Colors.
Use distinct bull/bear line colors you can read easily on your theme. The default lime/red scheme is chosen for clarity.
Practical examples
Momentum continuation (long).
Price is above your baseline (e.g., EMA 200). A B prints with a tight lower wick filter. Enter on close; stop under the signal HA low. Price pushes up in the next bars; you scale at 1R, trail the rest with EMA 20, and finally exit when a distant S appears or your trail is hit.
Breakout confirmation (short).
Following a range, price breaks down and prints an S with no upper wick. Enter short as the bar closes or on a subsequent break of the signal bar’s low. If the next bar immediately rejects and prints a bullish HA bar, your stop above the signal HA high limits damage. Otherwise, ride the move, harvesting partials as the red dashed line remains unviolated.
Alerts and automation
Set alerts to “Once Per Bar Close” for stability.
Bull ONE-SHOT fires when a valid buy prints (and Lock allows it).
Bear ONE-SHOT fires for sells analogously.
With Lock enabled, you avoid multiple pings in the same direction during a single leg—useful for webhooks or mobile notifications.
Reliability and limitations
The script calculates from completed bars and does not use higher-timeframe look-ahead or repainting tricks. Heikin-Ashi smoothing can lag turns slightly, which is expected and part of the design. In narrow ranges or whipsaw conditions, signals naturally thin out; if you must trade ranges, either tighten the wick filters and keep Lock ON, or add a trend/volatility filter (e.g., trade B only above EMA 200; S only below). Remember: this is an indicator, not a strategy. If you want exact statistics, port the triggers into a strategy and backtest with your chosen entry, stop, and exit rules.
Final notes
Wickless Heikin Ashi B/S \ is a precision timing tool: it waits for decisive, wickless HA bars, provides optional regime locking to reduce noise, and leaves clear price anchors on your chart for disciplined management. Use it with a simple framework—trend bias, fixed risk, and a straightforward exit plan—and it will keep your execution consistent without cluttering the screen or your decision-making.
Disclaimer: This indicator is for educational use and trade assistance only. It is not financial advice. You alone are responsible for your risk and results.
Enhance your trading precision and confidence with Wickless Heikin Ashi B/S ! 🚀
Happy trading
Chervolino
ATR Move Tracker v1.0# ATR Move Tracker v1.0 - Purpose & Use Guide
## What Is This Indicator?
ATR Move Tracker v1.0 is a trading tool that shows you whether a stock has moved "enough" for the day based on its historical patterns. It answers the question: **"Has this stock moved its typical daily amount yet?"**
## The Core Purpose
Think of each stock as having a "daily personality" - some stocks naturally move $1 per day, others move $5 per day. This indicator:
- **Shows the stock's Daily ATR** (its typical daily movement over 14 days)
- **Tracks Today's Range** (how much it's actually moved today)
- **Displays the gap** between typical and actual movement
- **Provides visual progress** toward completing its normal daily range
## Practical Trading Applications
### **Entry Timing**
- **Low Progress (0-30%)**: Stock hasn't moved much yet - potential opportunity if you expect movement
- **Medium Progress (30-70%)**: Stock is moving normally - good for trend following
- **High Progress (70-100%+**: Stock has moved a lot - be cautious of exhaustion
### **Exit Planning**
- When the progress bar turns **green (100%+)**, the stock has exceeded its typical daily movement
- Consider taking profits or tightening stops when Daily ATR is reached or exceeded
- Use remaining "Need" amount to set realistic profit targets
### **Risk Management**
- **Today's Range much smaller than Daily ATR**: Potential for more volatility
- **Today's Range exceeds Daily ATR**: Stock may be overextended for the day
- Adjust position sizes based on how much of the Daily ATR has been "used up"
## Real-World Example
**Stock XYZ at 2:00 PM shows:**
- Daily ATR: $4.00 (typically moves $4/day)
- Daily Move: $2.50 (62%)
- Need: $1.50
- Progress Bar: 62% filled (orange)
**Interpretation:** XYZ has moved $2.50 of its typical $4.00 daily range. There's potentially $1.50 more movement available based on historical patterns.
## Key Benefits
✅ **Context for Price Movement**: Know if today's action is normal or unusual
✅ **Better Position Sizing**: Trade smaller when Daily ATR is nearly reached
✅ **Realistic Profit Targets**: Base targets on remaining Daily ATR potential
✅ **Works on All Timeframes**: See daily context even on 1-minute charts
✅ **Fully Customizable**: Position, colors, and ATR period adjustable
## Who Should Use This
- **Day Traders**: Perfect for intraday volatility context
- **Swing Traders**: Helps time entries based on daily movement patterns
- **Options Traders**: Understand if implied volatility expectations are being met
- **Any Trader**: Who wants to trade with historical volatility context
This isn't a buy/sell signal - it's **volatility intelligence** that helps you make better trading decisions based on what's normal for each stock!
RSI MA Cross + Divergence Signal (V2) Core Logic
RSI + Moving Average
The script calculates a standard RSI (default 14).
It then overlays a moving average (SMA/EMA/WMA, default 9).
When RSI crosses above its MA → bullish momentum.
When RSI crosses below its MA → bearish momentum.
Divergence Filter
Signals are only valid if there’s confirmed divergence:
Bullish divergence: Price makes a lower low, RSI makes a higher low.
Bearish divergence: Price makes a higher high, RSI makes a lower high.
Overbought / Oversold Filter
Optional extra:
Bullish signals only valid if RSI ≤ 30 (oversold).
Bearish signals only valid if RSI ≥ 70 (overbought).
This ensures signals happen in “stretched” conditions.
Risk & Trade Management
Entries taken only when all conditions align.
Exits can be managed with ATR stops, partial take-profits, breakeven moves, and trailing stops (we coded these in the strategy version).
Cooldown, session filters, and daily loss guard to keep risk tight.
🔹 Strengths
✅ High selectivity: Combining RSI cross + divergence + OB/OS means signals are rare but higher quality.
✅ Great at catching reversals: Divergence highlights where price may be running out of steam.
✅ Risk management baked in: ATR stops + partial exits smooth out equity curve.
✅ Works across markets: ES, FX, crypto — anywhere RSI divergences are respected.
✅ Flexible: You can loosen/tighten filters depending on aggressiveness.
🔹 Weaknesses
❌ Lag from pivots: Divergence only confirms after a few bars → you enter late sometimes.
❌ Choppy in ranges: In sideways markets, RSI divergences appear often and whipsaw.
❌ Filters reduce signals: With all filters ON (divergence + OB/OS + trend + session), signals can be very rare — may under-trade.
❌ Not standalone: Needs higher-timeframe context (trend, liquidity pools) to avoid counter-trend entries.
🔹 Best Ways to Trade It
Use Higher Timeframe Bias
Run the strategy on 15m/1H, but only trade in direction of higher timeframe trend (e.g., 4H EMA).
Example: If daily is bullish → only take bullish divergences.
Pair With Structure
Look for signals at key zones: HTF support/resistance, VWAP, or FVGs.
Divergence + RSI cross inside an FVG is a strong entry trigger.
Adjust OB/OS for Volatility
For crypto/FX: use 35/65 instead of 30/70 (markets trend harder).
For ES/S&P: 30/70 works fine.
Risk Management Is King
Use partial exits: take profit at 1R, trail rest.
Size by % of equity (we coded this into the strategy).
Avoid News Spikes
Divergences break down around CPI, NFP, Fed announcements — stay flat.
🔹 When It Shines
Trending markets that make extended pushes → clean divergences.
Reversal zones (oversold → bullish bounce, overbought → bearish fade).
Swing trading (15m–4H) — less noise than 1m/5m scalping.
🔹 When to Avoid
Low volatility chop → lots of false divergences.
During high-impact news → RSI swings wildly.
In strong one-way trends without pullbacks — divergence keeps calling tops/bottoms too early.
✅ Summary:
This is a reversal-focused RSI divergence strategy with strict filters. It’s powerful when combined with higher-timeframe bias + structure confluence, but weak if traded blindly in choppy or news-driven conditions. Best to treat it as a precision entry trigger, not a full system — layer it on top of your FVG/ORB framework for maximum edge.
Pullback & ATR Trailing Strategy※日本語は英文の次に記載あります。
Overview
This indicator combines short-term RSI pullback/rebound signals with long-term RSI divergence to visualize potential buy and sell opportunities.
It also plots ATR-based trailing stops and partial take-profit lines, making it suitable for day trading and short-term trading.
Alerts are triggered when signal conditions are met.
Key Features
Detect short-term RSI pullbacks/rebounds (default 6 periods)
Detect divergences on long-term RSI
Visualize buy/sell signals with labels
Display ATR-based trailing stop and partial take-profit lines
Trigger alerts when conditions are met
Settings Explanation
Short-term RSI Length (rsiShortLen) Period for short-term RSI used to detect pullbacks or rebounds
Pullback Threshold (levelLow) RSI level below which a buy signal is considered
Rebound Threshold (levelHigh) RSI level above which a sell signal is considered
Long-term Timeframe (longTF) Timeframe used for divergence detection
Long-term RSI Length (longRSILen) Period for RSI on the long-term timeframe, used for divergence detection
Pivot Width Left / Right (pivotLeft / pivotRight)
Determines how we detect swing highs/lows (peaks and valleys).
For example, with pivotLeft=3 and pivotRight=3, a bar is considered a swing high if it is higher than the 3 bars to its left and 3 bars to its right.
Larger numbers detect only bigger swings, smaller numbers also detect smaller swings.
ATR Length (atrLen) Period for ATR calculation for trailing stops
ATR Multiplier (atrMult) Multiplier for ATR to calculate trailing stop distance
Partial Take-Profit Multiplier (tpMult) Multiplier to calculate half-profit level based on swing amplitude
Green line (Long Trail / translucent green)
ATR-based trailing stop line for long positions.
Used as a stop-loss or trailing stop for open buy trades.
Dark green line shows partial take-profit (TP), translucent green shows trailing stop level.
Red line (Short Trail / translucent red)
ATR-based trailing stop line for short positions.
Used as a stop-loss or trailing stop for open sell trades.
Dark red line shows partial take-profit (TP), translucent red shows trailing stop level.
Note: TP lines indicate partial take-profit targets, while ATR trailing lines indicate stop-loss/trailing stop levels if the price moves against the position.
日本語説明ーーーーーーーーーーーーーーーーーーーーーーーーーーーー
概要
このインジケーターは、短期RSIの押し目/戻りシグナルと、長期足RSIによるダイバージェンスを組み合わせて、買い・売りのチャンスを可視化します。
さらに、ATRベースのトレールストップラインや半分利確ラインも表示し、デイトレードや短期トレードに最適化しています。
シグナル条件に一致した場合にアラートも作動します。
主な機能
短期RSI（デフォルト6期間）で押し目・戻りを検出
長期足RSIでのダイバージェンスを検出
BUY/SELLラベルでシグナルを視覚化
ATRベースのトレールライン・半分利確ラインを表示
条件一致時にアラート発動
各設定の説明
短期RSI期間 (rsiShortLen) デイトレ用の短期RSIの期間。押し目や戻りのシグナルに使用
押し目閾値 (levelLow) RSIが下回ったら買いシグナル判定に使用
戻り閾値 (levelHigh) RSIが上回ったら売りシグナル判定に使用
長期足 (longTF) ダイバージェンス判定用の長期足の時間軸
長期RSI期間 (longRSILen) 長期足で計算するRSIの期間。ダイバージェンス判定に使用
左右ピボット幅 (pivotLeft / pivotRight) 高値や安値を「スイングの山・谷」として判定する時に使う幅です。
例えば pivotLeft=3, pivotRight=3 の場合、「左に3本、右に3本のローソク足より高い/低い点」をスイングの頂点や底と見なします。
数値を大きくすると大きな波だけを拾い、小さくすると小さな波も拾いやすくなります。
ATR期間 (atrLen) トレールライン計算用ATRの期間
ATR倍率 (atrMult) トレールラインの距離をATRに掛ける倍率
半分利確倍率 (tpMult) 押し目/戻り幅に対して半分利確ラインを設定する倍率
緑の線（Long Trail / 半透明緑）
ATRベースのトレールストップラインです。
買いポジション中の損切り目安やトレーリングストップとして使います。
緑の濃い線は半分利確ライン（TP）、薄い緑の線はトレールストップの位置を示します。
赤い線（Short Trail / 半透明赤）
ATRベースのトレールストップラインです。
売りポジション中の損切り目安やトレーリングストップとして使います。
赤の濃い線は半分利確ライン（TP）、薄い赤の線はトレールストップの位置を示します。
補足：TP（Take Profit）線は半分利確の目安で、ATRトレールラインはポジションが逆行した時の損切り目安です。
Expected Value Monte CarloI created this indicator after noticing that there was no Expected Value indicator here on TradingView.
The EVMC provides statistical Expected Value to what might happen in the future regarding the asset you are analyzing.
It uses 2 quantitative methods:
Historical Backtest to ground your analysis in long-term, factual data.
Monte Carlo Simulation to project a cone of probable future outcomes based on recent market behavior.
This gives you a data-driven edge to quantify risk, and make more informed trading decisions.
The indicator includes:
Dual analysis: Combines historical probability with forward-looking simulation.
Quantified projections: Provides the Expected Value ($ and %), Win Rate, and Sharpe Ratio for both methods.
Asset-aware: Automatically adjusts its calculations for Stocks (252 trading days) and Crypto (365 days) for mathematical accuracy.
The projection cone shows the mean expected path and the +/- 1 standard deviation range of outcomes.
No repainting
Calculation:
1. Historical Expected Value:
This is a systematic backtest over thousands of bars. It calculates the return Rᵢ for N past trades (buy-and-hold). The Historical EV is the simple average of these returns, giving a baseline performance measure.
Historical EV % = (Σ Rᵢ) / N
2. Monte Carlo Projection:
This projection uses the Geometric Brownian Motion (GBM) model to simulate thousands of future price paths based on the market's recent behavior.
It first measures the drift (μ), or recent trend, and volatility (σ), or recent risk, from the Projection Lookback period. It then projects a final return for each simulation using the core GBM formula:
Projected Return = exp( (μ - σ²/2)T + σ√T * Z ) - 1
(Where T is the time horizon and Z is a random variable for the simulation.)
The purple line on the chart is the average of all simulated outcomes (the Monte Carlo EV). The cone represents one standard deviation of those outcomes.
The dashed lines represent one standard deviation (+/- 1σ) from the average, forming a cone of probable outcomes. Roughly 68% of the simulated paths ended within this cone.
This projection answers the question: "If the recent trend and volatility continue, where is the price most likely to go?"
Here's how to read the indicator
Expected Value ($/%): Is my average trade profitable?
Win Rate: How often can I expect to be right?
Sharpe Ratio: Am I being adequately compensated for the risk I'm taking?
User Guide
Max trade duration (bars): This is your analysis timeframe. Are you interested in the probable outcome over the next month (21 bars), quarter (63 bars), or year (252 bars)?
Position size ($): Set this to your typical trade size to see the Expected Value in real dollar terms.
Projection lookback (bars): This is the most important input for the Monte Carlo model. A short lookback (e.g., 50) makes the projection highly sensitive to recent momentum. Use this to identify potential recency bias. A long lookback (e.g., 252) provides a more stable, long-term projection of trend and volatility.
Historical Lookback (bars): For the historical backtest, more data is always better. Use the maximum that your TradingView plan allows for the most statistically significant results.
Use TP/SL for Historical EV: Check this box to see how the historical performance would have changed if you had used a simple Take Profit and Stop Loss, rather than just holding for the full duration.
I hope you find this indicator useful and please let me know if you have any suggestions. 😊
ApicodeLibrary "Apicode"
percentToTicks(percent, from)
Converts a percentage of the average entry price or a specified price to ticks when the
strategy has an open position.
Parameters:
percent (float) : (series int/float) The percentage of the `from` price to express in ticks, e.g.,
a value of 50 represents 50% (half) of the price.
from (float) : (series int/float) Optional. The price from which to calculate a percentage and convert
to ticks. The default is `strategy.position_avg_price`.
Returns: (float) The number of ticks within the specified percentage of the `from` price if
the strategy has an open position. Otherwise, it returns `na`.
percentToPrice(percent, from)
Calculates the price value that is a specific percentage distance away from the average
entry price or a specified price when the strategy has an open position.
Parameters:
percent (float) : (series int/float) The percentage of the `from` price to use as the distance. If the value
is positive, the calculated price is above the `from` price. If negative, the result is
below the `from` price. For example, a value of 10 calculates the price 10% higher than
the `from` price.
from (float) : (series int/float) Optional. The price from which to calculate a percentage distance.
The default is `strategy.position_avg_price`.
Returns: (float) The price value at the specified `percentage` distance away from the `from` price
if the strategy has an open position. Otherwise, it returns `na`.
percentToCurrency(price, percent)
Parameters:
price (float) : (series int/float) The price from which to calculate the percentage.
percent (float) : (series int/float) The percentage of the `price` to calculate.
Returns: (float) The amount of the symbol's currency represented by the percentage of the specified
`price`.
percentProfit(exitPrice)
Calculates the expected profit/loss of the open position if it were to close at the
specified `exitPrice`, expressed as a percentage of the average entry price.
NOTE: This function may not return precise values for positions with multiple open trades
because it only uses the average entry price.
Parameters:
exitPrice (float) : (series int/float) The position's hypothetical closing price.
Returns: (float) The expected profit percentage from exiting the position at the `exitPrice`. If
there is no open position, it returns `na`.
priceToTicks(price)
Converts a price value to ticks.
Parameters:
price (float) : (series int/float) The price to convert.
Returns: (float) The value of the `price`, expressed in ticks.
ticksToPrice(ticks, from)
Calculates the price value at the specified number of ticks away from the average entry
price or a specified price when the strategy has an open position.
Parameters:
ticks (float) : (series int/float) The number of ticks away from the `from` price. If the value is positive,
the calculated price is above the `from` price. If negative, the result is below the `from`
price.
from (float) : (series int/float) Optional. The price to evaluate the tick distance from. The default is
`strategy.position_avg_price`.
Returns: (float) The price value at the specified number of ticks away from the `from` price if
the strategy has an open position. Otherwise, it returns `na`.
ticksToCurrency(ticks)
Converts a specified number of ticks to an amount of the symbol's currency.
Parameters:
ticks (float) : (series int/float) The number of ticks to convert.
Returns: (float) The amount of the symbol's currency represented by the tick distance.
ticksToStopLevel(ticks)
Calculates a stop-loss level using a specified tick distance from the position's average
entry price. A script can plot the returned value and use it as the `stop` argument in a
`strategy.exit()` call.
Parameters:
ticks (float) : (series int/float) The number of ticks from the position's average entry price to the
stop-loss level. If the position is long, the value represents the number of ticks *below*
the average entry price. If short, it represents the number of ticks *above* the price.
Returns: (float) The calculated stop-loss value for the open position. If there is no open position,
it returns `na`.
ticksToTpLevel(ticks)
Calculates a take-profit level using a specified tick distance from the position's average
entry price. A script can plot the returned value and use it as the `limit` argument in a
`strategy.exit()` call.
Parameters:
ticks (float) : (series int/float) The number of ticks from the position's average entry price to the
take-profit level. If the position is long, the value represents the number of ticks *above*
the average entry price. If short, it represents the number of ticks *below* the price.
Returns: (float) The calculated take-profit value for the open position. If there is no open
position, it returns `na`.
calcPositionSizeByStopLossTicks(stopLossTicks, riskPercent)
Calculates the entry quantity required to risk a specified percentage of the strategy's
current equity at a tick-based stop-loss level.
Parameters:
stopLossTicks (float) : (series int/float) The number of ticks in the stop-loss distance.
riskPercent (float) : (series int/float) The percentage of the strategy's equity to risk if a trade moves
`stopLossTicks` away from the entry price in the unfavorable direction.
Returns: (int) The number of contracts/shares/lots/units to use as the entry quantity to risk the
specified percentage of equity at the stop-loss level.
calcPositionSizeByStopLossPercent(stopLossPercent, riskPercent, entryPrice)
Calculates the entry quantity required to risk a specified percentage of the strategy's
current equity at a percent-based stop-loss level.
Parameters:
stopLossPercent (float) : (series int/float) The percentage of the `entryPrice` to use as the stop-loss distance.
riskPercent (float) : (series int/float) The percentage of the strategy's equity to risk if a trade moves
`stopLossPercent` of the `entryPrice` in the unfavorable direction.
entryPrice (float) : (series int/float) Optional. The entry price to use in the calculation. The default is
`close`.
Returns: (int) The number of contracts/shares/lots/units to use as the entry quantity to risk the
specified percentage of equity at the stop-loss level.
exitPercent(id, lossPercent, profitPercent, qty, qtyPercent, comment, alertMessage)
A wrapper for the `strategy.exit()` function designed for creating stop-loss and
take-profit orders at percentage distances away from the position's average entry price.
NOTE: This function calls `strategy.exit()` without a `from_entry` ID, so it creates exit
orders for *every* entry in an open position until the position closes. Therefore, using
this function when the strategy has a pyramiding value greater than 1 can lead to
unexpected results. See the "Exits for multiple entries" section of our User Manual's
"Strategies" page to learn more about this behavior.
Parameters:
id (string) : (series string) Optional. The identifier of the stop-loss/take-profit orders, which
corresponds to an exit ID in the strategy's trades after an order fills. The default is
`"Exit"`.
lossPercent (float) : (series int/float) The percentage of the position's average entry price to use as the
stop-loss distance. The function does not create a stop-loss order if the value is `na`.
profitPercent (float) : (series int/float) The percentage of the position's average entry price to use as the
take-profit distance. The function does not create a take-profit order if the value is `na`.
qty (float) : (series int/float) Optional. The number of contracts/lots/shares/units to close when an
exit order fills. If specified, the call uses this value instead of `qtyPercent` to
determine the order size. The exit orders reserve this quantity from the position, meaning
other orders from `strategy.exit()` cannot close this portion until the strategy fills or
cancels those orders. The default is `na`, which means the order size depends on the
`qtyPercent` value.
qtyPercent (float) : (series int/float) Optional. A value between 0 and 100 representing the percentage of the
open trade quantity to close when an exit order fills. The exit orders reserve this
percentage from the open trades, meaning other calls to this command cannot close this
portion until the strategy fills or cancels those orders. The percentage calculation
depends on the total size of the applicable open trades without considering the reserved
amount from other `strategy.exit()` calls. The call ignores this parameter if the `qty`
value is not `na`. The default is 100.
comment (string) : (series string) Optional. Additional notes on the filled order. If the value is specified
and not an empty "string", the Strategy Tester and the chart show this text for the order
instead of the specified `id`. The default is `na`.
alertMessage (string) : (series string) Optional. Custom text for the alert that fires when an order fills. If the
value is specified and not an empty "string", and the "Message" field of the "Create Alert"
dialog box contains the `{{strategy.order.alert_message}}` placeholder, the alert message
replaces the placeholder with this text. The default is `na`.
Returns: (void) The function does not return a usable value.
closeAllAtEndOfSession(comment, alertMessage)
A wrapper for the `strategy.close_all()` function designed to close all open trades with a
market order when the last bar in the current day's session closes. It uses the command's
`immediately` parameter to exit all trades at the last bar's `close` instead of the `open`
of the next session's first bar.
Parameters:
comment (string) : (series string) Optional. Additional notes on the filled order. If the value is specified
and not an empty "string", the Strategy Tester and the chart show this text for the order
instead of the automatically generated exit identifier. The default is `na`.
alertMessage (string) : (series string) Optional. Custom text for the alert that fires when an order fills. If the
value is specified and not an empty "string", and the "Message" field of the "Create Alert"
dialog box contains the `{{strategy.order.alert_message}}` placeholder, the alert message
replaces the placeholder with this text. The default is `na`.
Returns: (void) The function does not return a usable value.
closeAtEndOfSession(entryId, comment, alertMessage)
A wrapper for the `strategy.close()` function designed to close specific open trades with a
market order when the last bar in the current day's session closes. It uses the command's
`immediately` parameter to exit the trades at the last bar's `close` instead of the `open`
of the next session's first bar.
Parameters:
entryId (string)
comment (string) : (series string) Optional. Additional notes on the filled order. If the value is specified
and not an empty "string", the Strategy Tester and the chart show this text for the order
instead of the automatically generated exit identifier. The default is `na`.
alertMessage (string) : (series string) Optional. Custom text for the alert that fires when an order fills. If the
value is specified and not an empty "string", and the "Message" field of the "Create Alert"
dialog box contains the `{{strategy.order.alert_message}}` placeholder, the alert message
replaces the placeholder with this text. The default is `na`.
Returns: (void) The function does not return a usable value.
sortinoRatio(interestRate, forceCalc)
Calculates the Sortino ratio of the strategy based on realized monthly returns.
Parameters:
interestRate (simple float) : (simple int/float) Optional. The *annual* "risk-free" return percentage to compare against
strategy returns. The default is 2, meaning it uses an annual benchmark of 2%.
forceCalc (bool) : (series bool) Optional. A value of `true` forces the function to calculate the ratio on the
current bar. If the value is `false`, the function calculates the ratio only on the latest
available bar for efficiency. The default is `false`.
Returns: (float) The Sortino ratio, which estimates the strategy's excess return per unit of
downside volatility.
sharpeRatio(interestRate, forceCalc)
Calculates the Sharpe ratio of the strategy based on realized monthly returns.
Parameters:
interestRate (simple float) : (simple int/float) Optional. The *annual* "risk-free" return percentage to compare against
strategy returns. The default is 2, meaning it uses an annual benchmark of 2%.
forceCalc (bool) : (series bool) Optional. A value of `true` forces the function to calculate the ratio on the
current bar. If the value is `false`, the function calculates the ratio only on the latest
available bar for efficiency. The default is `false`.
Returns: (float) The Sortino ratio, which estimates the strategy's excess return per unit of
total volatility.
Trend-Strong Candle - 3 EMAs with Filters# Trend-Strong Candle - Professional Trading Indicator
## 📊 What It Does
Identifies high-probability entries by combining triple EMA trend analysis with strong candle detection. Only signals when all conditions align for maximum accuracy.
## 🎯 Core Features
- Triple EMA System: Fast (20) / Medium (50) / Slow (200) for trend confirmation
- Strong Candle Filter: ATR-based sizing ensures genuine momentum
- Advanced Filters: EMA close validation + trend stability checks
- Live Alerts: Instant notifications for real-time signals
- Session Filter: Trade only during active EU/US market hours
## ⚡ Quick Setup
Scalping (1-5min): Default settings + enable session filter
Day Trading (15-60min): Default settings work perfectly
Swing Trading (4H+): Increase ATR multiplier to 0.8-1.0
## 📈 Trading Rules
Long Signals: Green triangle below candle
- Strong bullish candle during confirmed uptrend
- All EMAs properly aligned (Fast > Medium > Slow)
Short Signals: Red triangle above candle
- Strong bearish candle during confirmed downtrend
- All EMAs properly aligned (Fast < Medium < Slow)
## ⚠️ Critical Success Factors
1. Always Verify the Trend Yourself
The indicator helps identify signals, but YOU must confirm the larger trend context. Check higher timeframes and overall market structure before entering.
2. Understand the "Big Players"
Strong candles in trend direction usually come from institutional money (banks, funds, algorithms). These create the momentum that retail traders can follow. The indicator catches these institutional moves.
3. Distance to Next Value Level
NEVER enter if price is too close to major resistance/support levels:
- Check distance to round numbers (1.1000, 1.1050, etc.)
- Ensure at least 20-30 pips room to next key level
- You need space for profit - tight levels = limited upside
4. Risk Management
- Stop Loss: 1-2 ATR from entry
- Take Profit: 2-3 ATR target (minimum 1:2 R/R)
- Position Size: Risk max 1-2% per trade
## 💡 Pro Tips
- Best Sessions: London open (8-12 UTC) and NY open (13-17 UTC)
- Avoid: Major news, low liquidity periods, choppy markets
- Multiple Timeframes: Confirm signals on higher timeframe
- Value Levels: Always check daily/weekly support/resistance before entering
## 🎯 Success Formula
Trend Confirmation + Strong Institutional Candle + Distance to Value Levels = High Probability Trade
*
Remember: The indicator finds the signals, but successful trading requires your analysis of trend context and value level positioning. Trade smart, not just frequent.
Penguin Volatility State StrategyThe Penguin Volatility State Strategy is a comprehensive technical analysis framework designed to identify the underlying "state" or "regime" of the market. Instead of just providing simple buy or sell signals, its primary goal is to classify the market into one of four distinct states by combining trend, momentum, and volatility analysis.
The core idea is to trade only when these three elements align, focusing on periods of volatility expansion (a "squeeze breakout") that occur in the direction of a confirmed trend and are supported by strong momentum.
Key Components
The strategy is built upon two main engines
The Volatility Engine (Bollinger Bands vs. Keltner Channels)
This engine detects periods of rapidly increasing volatility. It measures the percentage difference (diff) between the upper bands of Bollinger Bands (which are based on standard deviation) and Keltner Channels (based on Average True Range). During a volatility "squeeze," both bands are close. When price breaks out, the Bollinger Band expands much faster than the Keltner Channel, causing the diff value to become positive. A positive diff signals a volatility breakout, which is the moment the strategy becomes active.
The Trend & Momentum Engine (Multi-EMA System)
This engine determines the market's direction and strength. It uses:
A Fast EMA (e.g., 12-period) and a Slow EMA (e.g., 26-period): The crossover of these two moving averages defines the primary, underlying trend (similar to a MACD).
An Ultra-Fast EMA (e.g., 2-period of ohlc4): This is used to measure the immediate, short-term momentum of the price.
The Four Market States
By combining the Trend and Momentum engines, the strategy categorizes the market into four visually distinct states, represented by the chart's background color. This is the most crucial aspect of the system.
💚 Green State: Strong Bullish
The primary trend is UP (Fast EMA > Slow EMA) AND the immediate momentum is STRONG (Price > Fast EMA).
Interpretation: This represents a healthy, robust uptrend where both the underlying trend and short-term price action are aligned. It is considered the safest condition for taking long positions.
❤️ Red State: Strong Bearish
Condition: The primary trend is DOWN (Fast EMA < Slow EMA) AND the immediate momentum is WEAK (Price < Fast EMA).
Interpretation: This represents a strong, confirmed downtrend. It is considered the safest condition for taking short positions.
💛 Yellow State: Weakening Bullish / Pullback
Condition: The primary trend is UP (Fast EMA > Slow EMA) BUT the immediate momentum is WEAK (Price < Fast EMA).
Interpretation: This is a critical warning signal for bulls. While the larger trend is still up, the short-term price action is showing weakness. This could be a minor pullback, a period of consolidation, or the very beginning of a trend reversal. Caution is advised.
💙 Blue State: Weakening Bearish / Relief Rally
Condition: The primary trend is DOWN (Fast EMA < Slow EMA) BUT the immediate momentum is STRONG (Price > Fast EMA).
Interpretation: This signals that a downtrend is losing steam. It often represents a short-covering rally (a "bear market rally") or the first potential sign of a market bottom. Bears should be cautious and consider taking profits.
How the Strategy Functions
The strategy uses these four states as its foundation for making trading decisions. The entry and exit arrows (Long, Short, Close) are generated based on a set of rules that can be customized by the user. For instance, a trader can configure the strategy to
Only take long trades during the Green State.
Require a confirmed volatility breakout (diff > 0) before entering a trade.
Use the "RSI on Diff" indicator to ensure that the breakout is supported by accelerating momentum.
Summary
In essence, the Penguin Volatility State Strategy provides a powerful "dashboard" for viewing the market. It moves beyond simple indicators to offer a contextual understanding of price action. By waiting for the alignment of Trend (the State), Volatility (the Breakout), and Momentum (the Acceleration), it helps traders to identify higher-probability setups and, just as importantly, to know when it is better to stay out of the market.
License / disclaimer
© waranyu.trkm — MIT License. Educational use only; not financial advice.
Recovery StrategyDescription:
The Recovery Strategy is a long-only trading system designed to capitalize on significant price drops from recent highs. It enters a position when the price falls 10% or more from the highest high over a 6-month lookback period and adds positions on further 2% drops, up to a maximum of 5 positions. Each trade is held for 6 months before exiting, regardless of profit or loss. The strategy uses margin to amplify position sizes, with a default leverage of 5:1 (20% margin requirement). All key parameters are customizable via inputs, allowing flexibility for different assets and timeframes. Visual markers indicate recent highs for reference.
How It Works:
Entry: Buys when the closing price drops 10% or more from the recent high (highest high in the lookback period, default 126 bars ~6 months). If already in a position, additional buys occur on further 2% drops (e.g., 12%, 14%, 16%, 18%), up to 5 positions (pyramiding).
Exit: Each trade exits after its own holding period (default 126 bars ~6 months), regardless of profit or loss. No stop loss or take-profit is used.
Margin: Uses leverage to control larger positions (default 20% margin, 5:1 leverage). The order size is a percentage of equity (default 100%), adjustable via inputs.
Visualization: Displays blue markers (without text) at new recent highs to highlight reference levels.
Inputs:
Lookback Period for High Peak (bars): Number of bars to look back for the recent high (default: 126, ~6 months on daily charts).
Initial Drop Percentage to Buy (%): Percentage drop from recent high to trigger the first buy (default: 10.0%).
Additional Drop Percentage to Buy (%): Further drop percentage to add positions (default: 2.0%).
Holding Period (bars): Number of bars to hold each position before selling (default: 126, ~6 months).
Order Size (% of Equity): Percentage of equity used per trade (default: 100%).
Margin for Long Positions (%): Percentage of position value covered by equity (default: 20%, equivalent to 5:1 leverage).
Usage:
Timeframe: Designed for daily charts (126 bars ~6 months). Adjust Lookback Period and Holding Period for other timeframes (e.g., 1008 hours for hourly charts, assuming 8 trading hours/day).
Assets: Suitable for stocks, ETFs, or other assets with significant price volatility. Test thoroughly on your chosen asset.
Settings: Customize inputs in the strategy settings to match your risk tolerance and market conditions. For example, lower Margin for Long Positions (e.g., to 10% for 10:1 leverage) to increase position sizes, but beware of higher risk.
Backtesting: Use TradingView’s Strategy Tester to evaluate performance. Check the “List of Trades” for skipped trades due to insufficient equity or margin requirements.
Risks and Considerations:
No Stop Loss: The strategy holds trades for the full 6 months without a stop loss, exposing it to significant drawdowns in prolonged downtrends.
Margin Risk: Leverage (default 5:1) amplifies both profits and losses. Ensure sufficient equity to cover margin requirements to avoid skipped trades or simulated margin calls.
Pyramiding: Up to 5 positions can be open simultaneously, increasing exposure. Adjust pyramiding in the code if fewer positions are desired (e.g., change to pyramiding=3).
Market Conditions: Performance depends on price drops and recoveries. Test on historical data to assess effectiveness in your market.
Broker Emulator: TradingView’s paper trading simulates margin but does not execute real margin trading. Results may differ in live trading due to broker-specific margin rules.
How to Use:
Add the strategy to your chart in TradingView.
Adjust input parameters in the settings panel to suit your asset, timeframe, and risk preferences.
Run a backtest in the Strategy Tester to evaluate performance.
Monitor open positions and margin levels in the Trading Panel to manage risk.
For live trading, consult your broker’s margin requirements and leverage policies, as TradingView’s simulation may not match real-world conditions.
Disclaimer:
This strategy is for educational purposes only and does not constitute financial advice. Trading involves significant risk, especially with leverage and no stop loss. Always backtest thoroughly and consult a financial advisor before using any strategy in live trading.
Weekly opening targets +-5%## Summary
This indicator automatically plots key percentage-based price levels above and below the current week's opening price. It is designed to provide traders with a clear map of potential intra-week support, resistance, and target zones based on clean, mathematical levels.
The script is lightweight and focuses on providing a clutter-free visual guide, making it easy to identify significant price areas at a glance.
## Features
Weekly Open Pivot: A central blue line clearly marks the opening price for the current week, acting as the primary baseline for all calculations.
Precise 1% Levels: The indicator calculates and draws horizontal lines at exact 1% increments away from the weekly open, covering a range from +/- 1% up to +/- 5%.
Color-Coded Zones: Levels above the weekly open are colored green (representing potential resistance or target zones), while levels below are colored red (representing potential support).
Real-Time Price Labels: To ensure clarity, clean labels are displayed on the right-hand side of the chart. Each label shows both its percentage deviation and the exact price, updating automatically with the latest data.
## How to Use
This tool is versatile, but here are a few common applications:
Identifying Support & Resistance: The primary use is to watch for price reactions at these calculated levels. A bounce off a lower (red) level could signal support, while a rejection from an upper (green) level could signal resistance.
Setting Profit Targets: The levels serve as excellent, non-subjective price targets. For example, if you enter a long position near the weekly open, the +1% and +2% levels are logical areas to consider taking profit.
Gauging Weekly Momentum: The distance price travels between these levels can help gauge the strength of the weekly trend. Consistently breaking through levels indicates strong momentum, while failing to do so may suggest consolidation.
This indicator is particularly useful for day traders and swing traders who use the weekly open as a key reference point for market sentiment and direction.
Marubozu Detector with Dynamic SL/TP
Strategy Overview:
This indicator detects a "Marubozu" bullish pattern or a “Marubozu” bearish pattern to suggest potential buy and sell opportunities. It uses dynamic Stop Loss (SL) and Take Profit (TP) management, based on either market volatility (ATR) or liquidity zones.
This tool is intended for educational and informational purposes only.
Key Features:
Entry: Based on detecting Marubozu bullish or bearish candle pattern.
Exit: Targets are managed through ATR multiples or previous liquidity levels (swing highs or swing lows).
Smart Liquidity: Optionally identify deeper liquidity targets.
Full Alerts: Buy and Sell signals supported with customizable alerts.
Visualized Trades: Entry, SL, and TP levels are plotted on the chart.
User Inputs:
ATR Length, ATR Multipliers
Take Profit Mode (Liquidity/ATR)
Swing Lookback and Strength
Toggleable Buy/Sell alerts
All Time Frames
📖 How to Use:
Add the Indicator:
Apply the script to your chart from the TradingView indicators panel.
Look for Buy Signals:
A buy signal is triggered when the script detects a "Marubozu" bullish pattern.
Entry, Stop Loss, and Take Profit levels are plotted automatically.
Look for Sell Signals:
A Sell signal is triggered when the script detects a "Marubozu" bearish pattern.
Entry, Stop Loss, and Take Profit levels are plotted automatically.
Choose Take Profit Mode:
ATR Mode: TP is based on a volatility target.
Liquidity Mode: TP is based on past swing highs.
Set Alerts (Optional):
Enable Buy/Sell alerts in the settings to receive real-time notifications.
Practice First:
Always backtest and paper trade before live use.
📜 Disclaimer:
This script does not offer financial advice.
No guarantees of profit or performance are made.
Use in demo accounts or backtesting first.
Always practice proper risk management and seek advice from licensed professionals if needed.
✅ Script Compliance:
This script is designed in full accordance with TradingView’s House Rules for educational tools.
No financial advice is provided, no performance is guaranteed, and users are encouraged to backtest thoroughly.
SPX EMA 9/21 + VWAP Strategy1. Temporality: 2 minutes.
2. EMA 9 and EMA 21:
• Purchase Call: when EMA 9 crosses up EMA 21 and the price is > VWAP.
• Put : when EMA 9 crosses down EMA 21 and the price is < VWAP.
3. Stop and Take Profit:
• Stop: candle closure on the other side of the VWAP.
• TP: configurable in points (e.g. +10 pts, +20 pts) or up to the opposite crossing of EMAs.
• Long enters when EMA 9 crosses up 21 and the price is above VWAP.
• Short enters when the EMA 9 crosses down the 21 and the price is below VWAP.
• TP and SL in SPX points (configurable in inputs).
• You can run in 2 minutes on SPX.
Sunmool's Silver Bullet Model FinderICT Silver Bullet Model Indicator - Complete Guide
📈 Overview
The ICT Silver Bullet Model indicator is a supplementary tool for utilizing ICT's (Inner Circle Trader) market structure analysis techniques. This indicator detects institutional liquidity hunting patterns and automatically identifies structural levels, helping traders analyze market structure more effectively.
🎯 Core Features
1. Structural Level Identification
STL (Short Term Low): Recent support levels formed in the short term
STH (Short Term High): Recent resistance levels formed in the short term
ITL (Intermediate Term Low): Stronger support levels with more significance
ITH (Intermediate Term High): Stronger resistance levels with more significance
2. Kill Zone Time Display
London Kill Zone: 02:00-05:00 (default)
New York Kill Zone: 08:30-11:00 (default)
These are the most active trading hours for institutional players where significant price movements occur
3. Smart Sweep Detection
Bear Sweep (🔻): Pattern where price sweeps below lows then recovers - Simply indicates sweep occurrence
Bull Sweep (🔺): Pattern where price sweeps above highs then declines - Simply indicates sweep occurrence
Important: Sweep labels only mark liquidity hunting locations, not directional bias.
🔧 Configuration Parameters
Basic Settings
Sweep Detection Lookback: Number of candles for sweep detection (default: 20)
Structure Point Lookback: Number of candles for structural point detection (default: 10)
Sweep Threshold: Percentage threshold for sweep validation (default: 0.1%)
Time Settings
London Kill Zone: Active hours for London session
New York Kill Zone: Active hours for New York session
Visualization Settings
Customizable colors for each level type
Enable/disable alert notifications
📊 How to Use
1. Chart Setup
Most effective on 1-minute to 1-hour timeframes
Recommended for major currency pairs (EUR/USD, GBP/USD, etc.)
Also applicable to cryptocurrencies and indices
2. Signal Interpretation
🔻 Bear Sweep / 🔺 Bull Sweep Labels
Simply indicate liquidity hunting occurrence points
Not directional bias indicators
Reference for understanding overall context on HTF
🟢 Silver Bullet Long (Huge Green Triangle)
After Bear Sweep occurrence
Within Kill Zone timeframe
Current price positioned above swept level
→ Actual BUY entry signal
🔴 Silver Bullet Short (Huge Red Triangle)
After Bull Sweep occurrence
Within Kill Zone timeframe
Current price positioned below swept level
→ Actual SELL entry signal
3. Risk Management
Use swept levels as stop-loss reference points
Approach signals outside Kill Zone hours with caution
Recommended to use alongside other technical analysis tools
💡 Trading Strategies
Silver Bullet Strategy
Preparation Phase: Monitor charts 30 minutes before Kill Zone
Sweep Observation: Identify liquidity hunting points with 🔻🔺 labels (reference only)
Entry: Enter ONLY when huge triangle Silver Bullet signal appears within Kill Zone
Take Profit: Target opposite structural level or 1:2 reward ratio
Stop Loss: Beyond the swept level
Important: Small sweep labels are NOT trading signals!
Multi-Timeframe Approach
Step 1: HTF (Higher Time Frame) Sweep Reference
Observe 🔻🔺 sweep labels on 4-hour and daily charts
Reference only sweeps occurring at major structural levels
HTF sweeps are used to identify liquidity hunting points
Reference only, not for directional bias
Step 2: Transition to LTF (Lower Time Frame)
Move to 15-minute, 5-minute, and 1-minute charts
Analyze LTF with reference to HTF sweep information
Use STL, STH, ITL, ITH for precise entry point identification
Structural levels on LTF are the core of actual trading decisions
Only huge triangle (Silver Bullet) signals are actual entry signals
Recommended Usage
Identify overall sweep occurrence points on HTF (🔻🔺 labels)
Use this indicator on LTF to identify structural levels
Reference only huge triangle signals for actual trading during Kill Zone
Small sweep labels (🔻🔺) are for reference only, not entry signals
📋 Information Table Interpretation
Real-time information in the top-right table:
Kill Zone Status: Current active session status
Level Counts: Number of each structural level type
⚠️ Important Disclaimers
Backtesting results do not guarantee future performance
Exercise caution during high market volatility periods
Always apply proper risk management
Recommend comprehensive analysis with other analytical tools
🎓 Learning Resources
Study original ICT concepts through free YouTube educational content
Research Market Structure analysis techniques
Optimize through backtesting for personal use
🔬 Technical Implementation
Algorithm Logic
Pivot Point Detection: Uses TradingView's built-in pivot functions to identify swing highs and lows
Classification System: Automatically categorizes levels based on recent price action frequency
Sweep Validation: Confirms legitimate sweeps through price action analysis
Time-Based Filtering: Prioritizes signals during institutional active hours
Performance Optimization
Efficient array management prevents memory overflow
Dynamic level cleanup maintains chart clarity
Real-time calculation ensures minimal lag
🛠️ Customization Tips
Adjust lookback periods based on market volatility
Modify kill zone times for different market sessions
Experiment with sweep threshold for different instruments
Color-code levels according to personal preference
📈 Expected Outcomes
When properly implemented, this indicator can help traders:
Identify high-probability reversal points
Time entries with institutional flow
Reduce false signals through kill zone filtering
Improve risk-to-reward ratios
This indicator automates ICT's concepts into a user-friendly tool that can be enhanced through continuous learning and practical application. Success depends on understanding the underlying market structure principles and combining them with proper risk management techniques.
HMK-2 | PCA-1 + Rejim + Chebyshev + VWAP (Input'lu, v6)📌 HMK-2 | PCA-1 + Regime + Chebyshev + VWAP Strategy
1️⃣ Core Structure
Instead of relying on a single indicator, this system uses the Z-Score normalized average of three oscillators (RSI, MFI, ROC).
Signal (PCA-1):
RSI(14), MFI(14), ROC(5) → each is converted into a z-score.
Their average becomes the “composite signal,” our PCA-1 value.
Trend direction: If the Z-score EMA is rising → trend UP. If falling → trend DOWN.
2️⃣ Side Filters
Regime Filter (ADX + EMA)
ADX is calculated manually.
If ADX > 20 → trend exists → a 50-period EMA of this value smooths it.
This turns “trend regime” into a probability between 0–1.
Chebyshev Filter
A return series is checked against mean ± k*sigma bands.
If the return is within this band → valid signal. Extreme moves are filtered out.
VWAP Filter
Long trades: price must be above VWAP.
Short trades: price must be below VWAP.
Trades are only taken on the correct side of institutional cost averages.
3️⃣ Entry Conditions
Long:
PCA-1 signal crosses above threshold.
Trend Up + Regime OK + Chebyshev OK + Above VWAP.
Short:
PCA-1 signal crosses below threshold.
Trend Down + Regime OK + Chebyshev OK + Below VWAP.
4️⃣ Exit Mechanism
Main Exit: ATR-based stop/target.
Stop = entry price – ATR × (SL factor).
Take profit = entry price + ATR × (TP factor).
Additional Exit:
If price crosses to the opposite side of VWAP.
If PCA-1 signal crosses zero.
👉 Prevents trades from being locked, makes exits adaptive.
5️⃣ Labels / Visualization
AL / SHORT → entry points.
SAT / COVER → exit points.
VWAP line plotted in blue.
🧩 Strategy Features
Optimizable parameters:
Z-window (zWin)
Threshold
Chebyshev factor
ATR stop/target multipliers
This system works with:
Disciplined core (PCA-1 signal)
Triple protection (Regime + Chebyshev + VWAP)
Adaptive exits (ATR + VWAP/signal cross)
👉 Not a “single-indicator robot,” but a multi-filtered trade direction engine.
💡 Final Note
This is a base model of the system — open for further development.
I’ve shared the logic to give you a roadmap.
If you spot errors, fix them → that’s how you’ll improve it.
Don’t waste time asking me questions — refine and build it better yourselves.
Wishing you profitable trades. Stay well 🙏
Instant Breakout Strategy with RSI & VWAPInstant Breakout Strategy with RSI & VWAP
This TradingView strategy (Pine Script v6) trades breakouts using pivot points, with optional filters for volume, momentum, RSI, and VWAP. It’s optimized for the 1-second timeframe.
Overview
The strategy identifies breakouts when price crosses above resistance (pivot highs) or below support (pivot lows). It can use basic pivot breakouts or add filters for stronger signals. Take-profit and stop-loss levels are set using ATR, and signals are shown on the chart.
Inputs
Left/Right Pivot Bars: Bars to detect pivots (default: 3). Lower values increase sensitivity.
Volume Surge Multiplier: Volume threshold vs. 20-period average (default: 1.5).
Momentum Threshold: Minimum % price change from bar open (default: 1%).
Take-Profit ATR Multiplier: ATR multiplier for take-profit (default: 9.0).
Stop-Loss ATR Multiplier: ATR multiplier for stop-loss (default: 1.0).
Use Filters: Enable/disable volume, momentum, RSI, and VWAP filters (default: off).
How It Works
1. Pivot Detection
Finds pivot highs (resistance) and lows (support) using ta.pivothigh and ta.pivotlow.
Tracks the latest pivot levels.
2. Volume Surge
Compares current volume to a 20-period volume average.
A surge occurs if volume exceeds the average times the multiplier.
3. Momentum
Measures price change from the bar’s open.
Bullish: Price rises >1% from open.
Bearish: Price falls >1% from open.
4. RSI and VWAP
RSI: 3-period RSI. Above 50 is bullish; below 50 is bearish.
VWAP: Price above VWAP is bullish; below is bearish.
5. ATR
14-period ATR sets take-profit (close ± atr * 9.0) and stop-loss (close ± atr * 1.0).
Trading Rules
Breakout Conditions
Bullish Breakout:
Price crosses above the latest pivot high.
With filters: Volume surge, bullish momentum, RSI > 50, price > VWAP.
Without filters: Only the crossover is needed.
Bearish Breakout:
Price crosses below the latest pivot low.
With filters: Volume surge, bearish momentum, RSI < 50, price < VWAP.
Without filters: Only the crossunder is needed.
Entries and Exits
Long: Enter on bullish breakout. Set take-profit and stop-loss. Close any short position.
Short: Enter on bearish breakout. Set take-profit and stop-loss. Close any long position.
Visuals
Signals: Green triangles (bullish) below bars, red triangles (bearish) above bars.
Pivot Levels: Green line (resistance), red line (support).
Indicators: RSI (blue, separate pane), VWAP (purple, on chart).
How to Use
Apply to a 1-second chart in TradingView for best results.
Adjust inputs (e.g., pivot bars, multipliers). Enable filters for stricter signals.
Watch for buy/sell triangles and monitor RSI/VWAP.
Use ATR-based take-profit/stop-loss for risk management.
Notes
Best on 1-second timeframe due to fast RSI and responsiveness.
Disable filters for more signals (less confirmation).
Backtest before live trading to check performance.
This strategy uses pivots, volume, momentum, RSI, and VWAP for clear breakout trades on the 1-second timeframe.
PowerTrend Pro Strategy – Gold OptimizedTired of false signals on Gold?
PowerTrend Pro combines VWAP, Supertrend, RSI, and smart MA filters with trailing stops & break-even logic to deliver high-probability trades on XAUUSD.
PowerTrend Pro Strategy is a professional-grade trading system designed to capture high-probability swing and intraday opportunities on XAUUSD (Gold) and other volatile markets.
🔑 Core Features
VWAP Anchoring – institutional fair value reference to filter trades.
Supertrend (ATR-based) – adaptive trend filter tuned for Gold’s volatility.
Multi-Timeframe RSI – confirms momentum alignment across intraday and higher timeframe.
EMA + SMA Combo – ensures trades follow strong directional bias, reducing false signals.
Dynamic Risk Management
Adjustable Take Profit / Stop Loss (%)
Trailing Stop that locks in profits on extended moves
Break-Even Logic (stop loss moves to entry once price is in profit)
⚡ Gold-Tuned Presets
XAUUSD 1H → tighter TP/SL & faster entries for active intraday trading.
XAUUSD 4H → wider ATR filter & trailing stops to capture bigger swings.
Generic Mode → works on Forex, Indices, and Crypto (fully customizable).
🎯 Why It Works
Gold is notoriously volatile — quick spikes wipe out weak strategies. PowerTrend Pro solves this by combining:
✅ Institutional bias (VWAP)
✅ Adaptive trend filter (Supertrend)
✅ Momentum confirmation (RSI MTF)
✅ Robust trend structure (EMA + SMA)
✅ Smart exits (TP, SL, trailing & breakeven)
This multi-layer confirmation makes entries stronger and keeps risk under control.
🛠️ Usage
Add the strategy to your chart.
Choose a preset (XAUUSD 1H, 4H, or Generic).
Run Strategy Tester for performance metrics.
Optimize TP/SL and ATR values for your broker & market conditions.
🔥 Pro Tip: Combine this strategy with a session filter (London/NY overlap) or volume confirmation to boost accuracy in Gold.
Elliott Wave - Impulse + Corrective Detector (Demo) เทคนิคการใช้
สำหรับมือใหม่
ดูเฉพาะ Impulse Wave ก่อน
เทรดตาม direction ของ impulse
ใช้ Fibonacci เป็น support/resistance
สำหรับ Advanced
ใช้ Corrective Wave หาจุด reversal
รวม Triangle กับ breakout strategy
ใช้ Complex correction วางแผนระยะยาว
⚙️ การปรับแต่ง
ถ้าเจอ Pattern น้อยเกินไป
ลด Swing Length เป็น 3-4
เพิ่ม Max History เป็น 500
ถ้าเจอ Pattern เยอะเกินไป
เพิ่ม Swing Length เป็น 8-12
ปิด patterns ที่ไม่ต้องการ
สำหรับ Timeframe ต่างๆ
H1-H4: Swing Length = 5-8
Daily: Swing Length = 3-5
Weekly: Swing Length = 2-3
⚠️ ข้อควรระวัง
Elliott Wave เป็น subjective analysis
ใช้ร่วมกับ indicators อื่นๆ
Backtest ก่อนใช้เงินจริง
Pattern อาจเปลี่ยนได้ตลอดเวลา
🎓 สรุป
โค้ดนี้เป็นเครื่องมือช่วยวิเคราะห์ Elliott Wave ที่:
✅ ใช้งานง่าย
✅ ตรวจจับอัตโนมัติ
✅ มี confidence scoring
✅ แสดงผล Fibonacci levels
✅ ส่ง alerts เรียลไทม์
เหมาะสำหรับ: Trader ที่ต้องการใช้ Elliott Wave ในการวิเคราะห์เทคนิค แต่ไม่มีเวลานั่งหา pattern เอง
💡 Usage Tips
For Beginners
Focus on Impulse Waves first
Trade in the direction of impulse
Use Fibonacci as support/resistance levels
For Advanced Users
Use Corrective Waves to find reversal points
Combine Triangles with breakout strategies
Use Complex corrections for long-term planning
⚙️ Customization
If You See Too Few Patterns
Decrease Swing Length to 3-4
Increase Max History to 500
If You See Too Many Patterns
Increase Swing Length to 8-12
Turn off unwanted pattern types
For Different Timeframes
H1-H4: Swing Length = 5-8
Daily: Swing Length = 3-5
Weekly: Swing Length = 2-3
⚠️ Important Warnings
Elliott Wave is subjective analysis
Use with other technical indicators
Backtest before using real money
Patterns can change at any time
🔧 Troubleshooting
No Patterns Showing
Check if you have enough price history
Adjust Swing Length settings
Make sure pattern detection is enabled
Too Many False Signals
Increase confidence threshold requirements
Use higher timeframes
Combine with trend analysis
Performance Issues
Reduce Max History setting
Turn off unnecessary visual elements
Use on liquid markets only
📈 Trading Applications
Entry Strategies
Wave 3 Entry: After Wave 2 completion (61.8%-78.6% retracement)
Wave 5 Target: Equal to Wave 1 or Fibonacci extensions
Corrective Bounce: Trade reversals at C wave completion
Risk Management
Stop Loss: Beyond pattern invalidation levels
Take Profit: Fibonacci extension targets
Position Sizing: Based on pattern confidence
🎓 Summary
This code is an Elliott Wave analysis tool that offers:
✅ Easy to use interface
✅ Automatic pattern detection
✅ Confidence scoring system
✅ Fibonacci level display
✅ Real-time alerts
Perfect for: Traders who want to use Elliott Wave analysis but don't have time to manually identify patterns.
📚 Quick Reference
Pattern Hierarchy (Most to Least Reliable)
Impulse Waves (90% confidence)
Expanded Flats (85% confidence)
Zigzags (80% confidence)
Triangles (75% confidence)
Complex Corrections (70% confidence)
Best Practices
Start with higher timeframes for main trend
Use lower timeframes for precise entries
Always confirm with volume and momentum
Don't trade against strong fundamental news
Keep a trading journal to track performance
Remember: Elliott Wave is an art as much as a science. This tool helps identify potential patterns, but always use your judgment and additional analysis before making trading decisions.
Crypto Pulse Signals+ Precision
Crypto Pulse Signals
Institutional-grade background signals for BTC/ETH low-timeframe trading (2m/5m/15m).
🔵 BLUE TINT = Valid LONG signal (enter when candle closes)
🔴 RED TINT = Valid SHORT signal (enter when candle closes)
🌫️ NO TINT = No signal (avoid trading)
✅ BTC Momentum Filter: ETH signals only fire when BTC confirms (avoids 78% of fakeouts)
✅ Volatility-Adaptive: Signals auto-adjust to market conditions (no manual tuning)
✅ Dark Mode Optimized: Perfect contrast on all chart themes
Pro Trading Protocol:
Trade ONLY during NY/London overlap (12-16 UTC)
Enter on candle close when tint appears
Stop loss: Below/above signal candle's wick
Take profit: 1.8x risk (68% win rate in backtests)
Based on live trading during 2024 bull run - no repaint, no lag.
🔍 Why This Description Converts
Element Purpose
Clear visual cues "🔵 BLUE TINT = LONG" works instantly for scanners
BTC filter emphasis Highlights institutional edge (ETH traders' #1 pain point)
Time-specific protocol Filters out low-probability Asian session signals
Backtested stats Builds credibility without hype ("68% win rate" = believable)
Dark mode mention Targets 83% of crypto traders who use dark charts
📈 Real Dark Mode Performance
(Tested on TradingView Dark Theme - ETH/USDT 5m chart)
UTC Time Signal Color Visibility Result
13:27 🔵 LONG Perfect contrast against black background +4.1% in 11 min
15:42 🔴 SHORT Red pops without bleeding into red candles -3.7% in 8 min
03:19 None Zero visual noise during Asian session Avoided 2 fakeouts
Pro Tip: On dark mode, the optimized #4FC3F7 blue creates a subtle "watermark" effect - visible in peripheral vision but never distracting from price action.
✅ How to Deploy
Paste code into Pine Editor
Apply to BTC/USDT or ETH/USDT chart (Binance/Kraken)
Set timeframe to 2m, 5m, or 15m
Trade signals ONLY between 12-16 UTC (NY/London overlap)
This is what professional crypto trading desks actually use - stripped of all noise, optimized for real screens, and battle-tested in volatile markets. No bottom indicators. No clutter. Just pure signals.
LANZ Strategy 6.0🔷 LANZ Strategy 6.0 — NY Session Entry Tool & Multi-Account Risk Manager
LANZ Strategy 6.0 - Is a trading tool designed to help traders plan, execute, and manage operations with a focus on risk management, multi-account handling, and visual clarity.
It works exclusively on the 1-hour timeframe ⏳ and is optimized for the New York market opening dynamics.
🧠 Core Concept
The strategy identifies bullish trading opportunities based on the 09:00 NY candle. Once detected, it automatically calculates and draws:
EP (Entry Price) — The exact level where the trade setup triggers.
SL (Stop Loss) — Based on a customizable percentage of the candle's high–low range or wick extremes.
TP (Take Profit) — Calculated using your chosen Risk–Reward Ratio (e.g., 1:5, 1:3, etc.).
⚙️ Main Features
⏳ Time-Specific Execution
Operates only when the 09:00 NY candle closes bullish.
Ideal for traders who align with the New York Session market structure.
💰 Multi-Account Lot Size Management
Up to 5 independent accounts can be configured with their own capital and risk %, showing the exact lot size to use for each.
📏 Adaptive Risk Control
Supports both Forex and non-Forex assets (indices, gold, oil).
For non-Forex, you can manually define the pip value according to your broker’s specs.
🎨 Visual Trade Map
Automatically plots clean and easy-to-read EP, SL, and TP lines with customizable colors, styles, and thickness.
A floating information panel displays levels, pip distances, and lot sizes.
🔔 Real-Time Alerts
Alerts for:
Entry signal detection.
Stop Loss hit.
Take Profit hit.
Manual close at the defined session end.
📊 Example
If you trade GBPUSD with Account #1 set to $10,000 and 2% risk,
and the 09:00 NY candle closes bullish with SL = 30 pips and RR = 5:1:
EP, SL, and TP levels are drawn instantly.
Risk = $200 (2% of $10,000).
Lot size is calculated automatically.
All details are shown in the on-chart panel.
🛠️ How to Use
Load the indicator on a 1-hour chart.
Configure risk settings and account data.
Wait for the 09:00 NY candle to close bullish.
Use the displayed lot size and levels to execute your trade.
Let the tool alert you for SL, TP, or manual close.
⚠️ Disclaimer:
This script is for educational purposes only. It does not guarantee profits and past performance does not represent future results. Always manage your risk responsibly.
👨💻 Credits:
💡 Developed by: LANZ
🧠 Execution Model & Logic Design: LANZ
📅 Designed for: 1H timeframe and NY-based entries
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
