Stock Tech Bot One ViewTechnical indicators are not limited. Hence, here is another indicator with the combination of OBV, RSI, and MACD along with support, and resistance that follows the price while honoring the moving average of 200, 90 & 50.
The default lookback period of this indicator is 21 though it is changeable as per the user's desire.
The highest high and lowest low for the last 21 days lookback period proven to be the perfect Support & Resistance as the price of particular stock values are decided by market psychology. The support and resistance lines are very important to understand the market psychology which is very well proven with price action patterns and the lines are drawn based on,
Lower Extreme = 0.1 (Changeable)
Maximum Range = 21 days highest high - 21 days lowest low.
Support Line = 21 days lowest low + (Maximum Range * Lower Extreme)
Resistance Line = 21 days highest high - (Maximum Range * Lower Extreme)
RSI - Relative strength indicator is very famous to find the market momentum within the range of 0 - 100. Though the lookback period is changeable, the 14 days lookback period is the perfect match as the momentum of market movement for the last 3 weeks will always assist to identify the market regime. Here the momentum is just to highlight the indication (green up arrow under the candle for long and red down arrow above the candle for short) of market movement though it is not very important to consider if the price of the stock respect the support & resistance lines along with volume indicator (* = violet color).
OBV - Momentum:
The on-balance volume is always going indicator on any kind of tickers, which helps to identify the buying interest. Now, applying momentum on OBV with the positive movement for at least two consecutive days gives perfect confirmation for entry. A combination of the price along with this momentum(OBV) in the chart will help us to know the whipsaw in the price.
The Symbol "*" on top of each bar shows the market interest in that particular stock. If your ticker is fundamentally strong then you can see this "*" even when the market falls.
MACD:
One of the favorites and simple indicators widely used, where the thump of the rule is not to change the length even if it is allowed. It's OK to believe blindly in certain indicator and consider it while trading. That's why the indicator changes the bar color by following the MACD histogram.
Volume:
It may be the OBV works based on the open price and close price along with volume movement, it is wise to have the volume that is plotted along with price movement that should help you to decide whether the market is greedy or fearful.
The symbol "-" on top of each bar tells you a lot and don't ignore it.
Moving Average:
Moving average is a very good trend indicator as everyone considers seeing along with the price in the chart which is not omitted while we gauge the price movement alone with volume in this indicator. The 200, 90 & 50 MA's are everyone's favorite, and the same is plotted on the chart.
As explained above, the combination of all four indicators with price movement will give us very good confidence to take entry.
Candlestick Pattern:
You should admire the techniques of the candlestick pattern as you navigate the chart from right to left. Though there are a lot of patterns that exist, it is easy to enable and disable to view the signal as the label.
Further, last but not least, the exit always depends on individual conviction and how often the individual watch the price movement, if your conviction is strong then follow the down arrow red indication. If not, then exit with a trailing stop that indicates the bar with orange color.
Happy investing
Note: It is just a combination of multiple indicators and patterns to get one holistic view. So, the credit goes to all wise developers who publically published.
Поиск скриптов по запросу "entry"
Bitcoin trend RVI and Emastrategy with two emas and rvi.
Only long positions when fast ema above slow ema when rvi gives entry.
Only short positions when slow ema above fast ema when rvi gives entry.
+ Donchian ChannelsThis version of Donchian Channels uses two source options so that one can create a channel using highs and lows rather than one or the other or closes. My thinking was that this would create a more accurate portrayal of price action (or at least contain the greatest scope of it) as seen through the lens of a Donchian Channel. This was actually part of the genesis of my idea around my Ultimate Moving Average.
Besides the single top and bottom plot for the DC's extremities, I've enabled the ability to create outer bands with a variable width that the user can adjust to their preference. I think it's quite nice. I use it in the DC in my other non-overlay indicators.
Besides this additional functionality, the indicator has options to plot lines between the basis and the upper and lower bands, so, basically, splitting the upper and lower channel in half.
There is no magic number to the lookback. I chose 233 as default because it's a fibonacci sequence number and I'm more interested in using the DC like a very long period bias indicator, and the longer lookback gives a much wider window (because highs and lows are so spread apart) with which other faster indicators (supertrend, shorter period moving averages, etc.) can work without making the screen a clutter.
The color of the basis may also be made relevant to higher timeframe information. What I mean by this is that you can set it so that the basis of the current timeframe is colored based on the candle close of the higher timeframe of your choosing. If you're looking at an hourly chart, and you set the color to Daily, the basis will be colored based on the candle close (above or below the basis) of the previous day. If the previous daily close was above the basis, that positive color will be reflected in the basis, even if the current hourly candle closes are below the hourly basis. This could potentially be useful for setting a higher timeframe directional bias and reacting off price crossing the lower timeframe basis (or whatever your trigger for entering a trade might be). This is also optional in my Ultimate Moving Average indicator.
You can also set the entire indicator to whatever time frame you want if you want to see where the actual basis, or other levels are on that higher timeframe.
Further additions include fibonacci retracement levels. These are calculated off the high and the low of the Donchian Channels themselves.
You will see that there are only three retracement levels (.786, .705, .382), one of which is not a fib level, but what some people call the 'OTE,' or optimal trade entry. If you want more info on the OTE just web search it. So, why no .618 or .236? Reason being that the .618 overlaps the .382, and the .236 is extremely close to the .786. This sounds confusing, but the retracement levels I'm using are derived from the high and low, so it was unnecessary to have all five levels from each. I could have just calculated from the high, or just from the low, and used all the levels, but I chose to just calculate three levels from the high and three from the low because that gives a sort of mirror image balance, and that appeals to me, and the utility of the indicator is the same.
The plot lines are all colored, and I've filled certain zones between them. There is a center zone filled between both .382 levels, and an upper and lower zone filled between the .786 and either the high or the low.
If you like the colored zones, but don't like the plots because they cause screen compression, turn off the plots under the "style" tab.
There are alerts for candle closes across every line.
I should state that, regarding the fibs, obviously the length of the Channels is going to affect to what levels price retraces to. A shorter lookback means you will see more changes in highs and lows, and therefore retraces are often going to be full retraces within the bands unless price is trending hard. A longer lookback means you will see smaller retraces. Using this in conjunction with key high timeframe levels and/or a moving average can give great confidence in a trade entry. Additionally, if you have a short bias it may help in finding levels or entering a trade on a pullback. It could also be good for trade targets. But again, the lookback you choose for this indicator is going to dictate its use in the system you're building or already have. A 9 EMA and a 200 EMA, while fundamentally the same, are going to be used somewhat differently while doing your chart analysis.
Additional images below.
Same image as main, but with supertrend and my +UMA to help with chart analysis.
Image with the fib stuff turned on.
Zoomed out image with the same.
Shorter lookback period.
Zoomed in image of shorter lookback.
Daily HIGH/LOW strategyThis is a DAILY High/LOW strategy combined with a moving average and volume for more accuracy.
The rules are simple :
For long if we had a cross of the high with the previous high and close of the candle is above moving average and chaikin money flow volume is positive we have a long entry.
We exit when we cross down the moving average with the close of the candle.
For short if we had a crossdown of the low with the previous low and close of the candle is below moving average and chaikin money flow volume is negative we have a short entry.
We exit when we cross above the moving average with the close of the candle.
This strategy has no risk management inside so use it with caution.
If you have any questions, let me know
Ichimoku + RSI Crypto trending strategyThis is a crypto trending strategy designed for big timeframes such as 3-4h+.
Its components are:
RSI
ICHIMOKU full pack
Heikin Ashi candles for logic calculation inside
Rules for entry.
For long : we have a long cross condition on ichimoku and price is above the ichimoku lines, and at the same time RSI value is > 50.
For long : we have a short cross condition on ichimoku and price is below the ichimoku lines, and at the same time RSI value is < 50.
Rules for exit
We exit whenever we receive an opposite signal of the initial entry.
SInce this strategy is using no risk management inside, I recommend to be careful with it .
If you have any questions, let me know !
Vortex HeikinThis indicator use macd crossover plus vortex and heikin candle to find the best spot entry.
There a lot to improve if you want, it's only a starting point.
You can change Vortex indicator with ADX indicator to find a better spot, but there could be more false entry.
Swing forex strategy 15minThis is a strategy made using BB+ RSI indicators that seems to work great with 15 min major pairs for FOREX.
THe rules for it are simple:
For long we enter when the close of our candle crosses upwards the lower line and rsi crossover the over sold line
We exit long when we have a short entry.
For short we enter when the close of the candle crosses downwards the top line and rsi cross under the over bought line
WE exit short when we have a long entry.
Careful, this strategy has no risk management inside.
If you have any questions let me know !
[JL] High-Low Five LayersI just want to setup alert easily so I made this script.
Display five layers from highest to lowest.
Default length is 120. When on hour chart it is the whole week.
For up trend, always below 40% to entry.
For dn trend, always above 60% to entry.
EBB & Flow: a multi-EMA-based BB cloudIntro
This is an idea evolved out of the market maker method and EMA convergence, divergence, and mean reversion.
The market maker method informs us that the 5, 13, 50 and 200 EMAs are important to regulating price. Those EMA lengths are multiples of the 50 and 200 on lower major timeframes -- the 1 minute, 5, 15, 1H, 4H, 1D. I include the 21 because it is also a multiple and in crypto very often respected.
When market makers are testing price, they set their range and spike in the direction they test for liquidity. This can get chaotic. For instance, in a shorter time frame consolidation inside a bigger timeframe uptrend, it can be too easy to forget where you are in the many trends playing out.
When the EMAs are dragged over each other during normal price movement, you get these crisscrossing tracks of price, and the individual breaks can be hard to trace.
The range is what matters, ultimately, and the range is dynamic. In that case, the Bollinger Band is a great tool for detecting outliers in this case.
The Answer
So the answer this indicator seeks to give, is to look for outliers. This gives you a scalping strategy built on Traders Reality thinking and best put together with the PVSRA indicator, which I may include in this indicator just for the sake of concision, but they can work alongside each other or separately.
The key thing is the different EMA clouds, which are bollinger bands. Tight bands mean imminent breaks, favouring the trend. Vector candles out of a zone, pins to the low/high, etc. are all very relevant alongside this indicator.
You can also use it on its own and scalp the breaks of a cloud.
How it works
Each cloud is a standard deviation from their respective EMA, all in the same colour. The deviation multiple is 1.618 by default. Yes, fibonacci sequences are usually nonsense, but it works better with the BB than 2, 2.5 or 3.
Using just the clouds, you can see where each EMA is headed and how it behaves within the deviation of the others.
But that on its own isn't enough.
The indicator will also print snowflakes above and below the candle for notable outliers. It will be in the colour of the cloud it breaks, but only if that break is also breaking the smaller EMA clouds too.
The most snowflakes will be yellow because that's the 13 EMA. That one is dependent on nothing else and every break will print a snowflake. The 21 will be dependent on the 13. The 50 dependent on the 13 and 21 breaks. The 200 the most important.
For example, if the 200 EMA-BB or EBB is broken at the upper band, deviating by more than 162% of price over a 200 period EMA, and that break is not above the 50 EMA cloud, there will be no snowflake. However, if it exceeds the 13, 21, 50, and 200 clouds, then a purple snowflake will appear above the bar.
Any snowflake is an extreme in price. The purple is an especially good point of entry. That doesn't mean it is a perfect entry. You can build position from it, though, and be relatively certain of a price correction in the near future, because not only was this major EMA cloud violated, but all of the smaller ones too.
Reminder
You still need your PVSRA and candlesticks. This indicator on its own may have a nice hit rate for scalping and building position, as an alternative to the TDI or alongside it, but it is not enough on its own, just like the TDI.
Enjoy!
RSI Divergence X Ichimoku Cloud X 200EMAHi all,
This script is a combination of the RSI Divergence Strategy combined with Ichimoku Cloud and 200 EMA .
A long position is entered only when the RSI identifies a bullish divergence (either regular or hidden), and that the Ichimoku Cloud is above the 200 EMA . This is to ensure that there is a confirmation of a bullish trend before an entry.
Similarly, a short position is entered only when the RSI identified a bearish divergence (either regular or hidden), and that the Ichimoku Cloud is below the 200 EMA . This is to ensure that there is a confirmation of a bearish trend before an entry.
I find that this script works best on Intraday charts.
This is just a simple script I built on my third attempt of backtesting strategies on TradingView. Do give it a go and let me know if you guys have any feedback or comments about it. Happy trading!
ScalpyScalpy is made up of a 2 main parts.
- The cloud comprising of a 10 period SMA and a 30 period SMA.
- When the cloud is green you should be looking for long entries.
- When the cloud is red you should be looking for short entries.
- Price is most bullish above a green cloud and most bearish below a red cloud.
- Being within the cloud indicates indecision.
The blue and white lines on the indicator show the relationship between price and momentum.
They can be used to spot reversals in two ways:
- The first is a divergence between price (blue line) and RSI (white line)
- If the price makes a lower low but the RSI makes a higher low this shows the trend is weakening and may be reversing soon (as can be seen by the two yellow lines on the chart).
The second is a simple crossover:
- When the white line crosses the blue line to the upside this signals a long entry.
- When the white line crosses the blue line to the downside this signals a short entry.
Amazing Crossover System - 100+ pips per day!I got the main concept for this system on another site. While I have made one important change, I must stress that the heart of this system was created by someone else! We must give credit where credit is due!
Y'all know baby pips. @ForexPhantom published about this system and did both back and forward test around 10 years ago.
I found it on the sit and now I put it to code to see how it performs. I assume 10 points spread for every trade. I use Renesource or AxiTrader to get the low spreads.
There are 2 mods, the single trades and constant trading on the direction.
Main concept
Indicators
5 EMA -- YELLOW
10 EMA -- RED
RSI (10 - Apply to Median Price: HL/2) -- One level at 50.
TIME FRAME
1 Hour Only (very important!)
PAIRS
Virtually any pair seems to work as this is strictly technical analysis.
I recommend sticking to the main currencies and avoiding cross currencies (just his preference).
WHEN TO ENTER A TRADE
Enter LONG when the Yellow EMA crosses the Red EMA from underneath.
RSI must be approaching 50 from the BOTTOM and cross 50 to warrant entry.
Enter SHORT when the Yellow EMA crosses the Red EMA from the top.
RSI must be approaching 50 from the TOP and cross 50 to warrant entry.
I've attached a picture which demonstrates all these conditions.
That's it!
f.bpcdn.co
Build A BotThis is the Robot we built during the 60 Minute Build-A-Bot webinar on September 12, 2018. We had a great time, and a lot of participation and the best part was that we finished up this robot and even ran a backtest in exactly 60 minutes! We built this robot based on recommendations and suggestions from those who were attending live. Lots of pieces in this robot, but you can always tinker with it, remove stuff, add things, whatever you want!
This version uses the CCI as a trigger for trade entry. The other version uses the Hull Moving Average as a trigger for trade entry.
Hoffman A/D BreakoutStudy based on Rob Hoffman's Accumulation/Distribution Breakout strategy.
- Green circle on the top wick indicates a "Distribution" wick
- Red circle on the bottom wick indicates an "Accumulation" wick
- A distribution wick in an uptrend gets marked as a Key Resistance. This is marked with green crosses
- An Accumulation wick in a downtrend gets marked as a Key Support. This is marked with red crosses
- Breaking above the Key Resistance indicates a buy entry. This is marked by a green background.
- Breaking below the Key Support indicates a sell entry. This is marked by a red background
Liquidity Sweep + BOS Retest System — Prop Firm Edition🟦 Liquidity Sweep + BOS Retest System — Prop Firm Edition
A High-Probability Smart Money Strategy Built for NQ, ES, and Funding Accounts
🚀 Overview
The Liquidity Sweep + BOS Retest System (Prop Firm Edition) is a precision-engineered SMC strategy built specifically for prop firm traders. It mirrors institutional liquidity behavior and combines it with strict account-safe entry rules to help traders pass and maintain funding accounts with consistency.
Unlike typical indicators, this system waits for three confirmations — liquidity sweep, displacement, and a clean retest — before executing any trade. Every component is optimized for low drawdown, high R:R, and prop-firm-approved risk management.
Whether you’re trading Apex, TakeProfitTrader, FFF, or OneUp Trader, this system gives you a powerful mechanical framework that keeps you within rules while identifying the market’s highest-probability reversal zones.
🔥 Key Features
1. Liquidity Sweep Detection (Stop Hunt Logic)
Automatically identifies when price clears a previous swing high/low with a sweep confirmation candle.
✔ Filters noise
✔ Eliminates early entries
✔ Locks onto true liquidity grabs
2. Automatic Break of Structure (BOS) Confirmation
Price must show true displacement by breaking structure opposite the sweep direction.
✔ Confirms momentum shift
✔ Removes fake reversals
✔ Ensures institutional intent
3. Precision Retest Entry Model
The strategy enters only when price retests the BOS level at premium/discount pricing.
✔ Zero chasing
✔ Extremely tight stop loss placement
✔ Prop-firm-friendly controlled risk
4. Built-In Risk & Trade Management
SL set at swept liquidity
TP set by user-defined R:R multiplier
Optional session filter (NY Open by default)
One trade at a time (no pyramiding)
Automatically resets logic after each trade
This prevents overtrading — the #1 cause of evaluation and account breaches.
5. Designed for Prop Firm Futures Trading
This script is optimized for:
Trailing/static drawdown accounts
Micro contract precision
Funding evaluations
Low-risk, high-probability setups
Structured, rule-based execution
It reduces randomness and emotional trading by automating the highest-quality SMC sequence.
🎯 The Trading Model Behind the System
Step 1 — Liquidity Sweep
Price must take out a recent high/low and close back inside structure.
This confirms stop-hunting behavior and marks the beginning of a potential reversal.
Step 2 — BOS (Break of Structure)
Price must break the opposite side swing with a displacement candle. This validates a directional shift.
Step 3 — Retest Entry
The system waits for price to retrace into the BOS level and signal continuation.
This creates optimal R:R entry with minimal drawdown.
📈 Best Markets
NQ (NASDAQ Futures) – Highly recommended
ES, YM, RTY
Gold (XAUUSD)
FX majors
Crypto (with high volatility)
Works best on 1m, 2m, 5m, or 15m depending on your trading style.
🧠 Why Traders Love This System
✔ No signals until all confirmations align
✔ Reduces overtrading and emotional decisions
✔ Follows market structure instead of random indicators
✔ Perfect for maintaining long-term funded accounts
✔ Built around institutional-grade concepts
✔ Makes your trading consistent, calm, and rules-based
⚙️ Recommended Settings
Session: 06:30–08:00 MST (NY Open)
R:R: 1.5R – 3R
Contracts: Start with 1–2 micros
Markets: NQ for best structure & volume
📦 What’s Included
Complete strategy logic
All plots, labels, sweep markers & BOS alerts
BOS retest entry automation
Session filtering
Stop loss & take profit system
Full SMC logic pipeline
🏁 Summary
The Liquidity Sweep + BOS Retest System is a complete, prop-firm-ready, structure-based strategy that automates one of the cleanest and most reliable SMC entry models. It is designed to keep you safe, consistent, and rule-compliant while capturing premium institutional setups.
If you want to trade with confidence, discipline, and prop-firm precision — this system is for you.
Good Luck -BG
MTF Checklist DashboardMTF Checklist Dashboard
Overview
The MTF Checklist Dashboard is an advanced multi-timeframe analysis tool that provides traders with a comprehensive visual dashboard to analyze market conditions across six customizable timeframes simultaneously. This indicator combines multiple technical analysis methods, including Opening Range Breakouts (ORB), VWAP, EMAs, and daily price levels, to generate high-probability confluence-based trading signals.
Unlike traditional single-timeframe indicators, this dashboard displays all critical information in one organized table, allowing traders to instantly identify when multiple timeframes align for optimal entry and exit opportunities.
Key Features
Multi-Timeframe Analysis
Analyzes up to 6 timeframes simultaneously (default: 1m, 5m, 15m, 30m, 1h, 4h)
Fully customizable timeframe selection via comma-separated input
Color-coded cells for instant visual recognition (green=bullish, red=bearish, yellow=neutral)
Technical Indicators Tracked
Current and previous candle direction
Opening Range Breakout (ORB) positioning with custom period
VWAP relationship (above/below)
200 EMA positioning
Daily and previous day high/low proximity
EMA crossovers (9 vs 21, both vs 200)
Advanced Signal Filtering System
Confluence scoring: Requires multiple timeframes to align (3-6 timeframes)
Higher timeframe confirmation: Ensures 30m/1h/4h agreement
Volume filter: Confirms signals with above-average volume (1.5x default)
ATR volatility filter: Validates sufficient market movement
Session timing: Restricts signals to optimal trading hours (EST)
Momentum confirmation: Requires recent directional strength
Range positioning: Blocks signals near daily extremes
Candle strength: Validates strong directional candles (60%+ body ratio)
Visual Signals
Optional entry arrows (above/below bars)
Background color highlighting
Organized dashboard with real-time price levels
ORB range, current day, and previous day summary rows
Alert Conditions
JSON-formatted alerts for automated trading integration
Separate alerts for long entry, short entry, long exit, and short exit
Compatible with webhook automation systems
How To Use
Dashboard Interpretation
The dashboard displays a color-coded table with the following columns:
TF: Timeframe being analyzed
C: Current candle (Green=bullish, Red=bearish)
P: Previous candle (Green=bullish, Red=bearish)
ORB: Opening Range Breakout position (A=Above, B=Below, W=Within)
VWAP: Price vs VWAP (A=Above, B=Below)
E200: Price vs 200 EMA (A=Above, B=Below)
D Hi/Lo: Proximity to current day high/low (Hi/Lo/Mid)
PD Hi/Lo: Proximity to previous day high/low (Hi/Lo/Mid)
9 vs 21: EMA 9 vs EMA 21 relationship (A=9 above 21, B=9 below 21)
9&21 v200: Both EMAs vs 200 EMA (>>=both above, <<=both below, <>=mixed)
Signal Generation
Long Entry Signal triggers when:
Minimum number of timeframes show bullish alignment (default: 5 of 6)
Higher timeframes (30m/1h/4h) confirm direction (default: 2 of 3)
Price breaks above ORB high with sufficient distance
Volume exceeds average by specified multiplier
ATR shows adequate volatility
Trade occurs during optimal session hours
Recent momentum is upward
Price not too close to daily high
Strong bullish candle forms
Short Entry Signal uses opposite conditions
Exit Signals trigger when opposing timeframe confluence reaches threshold (default: 3 timeframes)
Recommended Workflow
Select your asset and primary trading timeframe
Observe the dashboard - Look for rows showing mostly green (bullish) or red (bearish)
Wait for alignment - The indicator will show arrows when confluence requirements are met
Check the bottom rows - Review ORB levels and daily ranges for context
Set alerts - Enable TradingView alerts using the built-in alert conditions
Manage risk - Use appropriate position sizing and stop losses based on ORB range or daily ATR
Settings Guide
Basic Settings
Timeframes: Enter comma-separated values (e.g., "1,5,15,30,60,240")
Show Header: Toggle column headers on/off
ORB Minutes: Set opening range period (default: 15 minutes)
Near % for daily highs/lows: Define proximity threshold (default: 0.20%)
Use close for comparisons: Compare using close vs current price
Dashboard Position: Choose from 9 screen positions
Confluence Filters
Minimum Timeframes Aligned: Set required confluence (3-6, default: 5)
Require Higher Timeframe Confirmation: Toggle HTF requirement on/off
Min Higher Timeframes: Specify HTF agreement needed (1-3, default: 2)
Volume Filter
Volume Confirmation: Enable/disable volume filtering
Volume vs Average: Set multiplier threshold (default: 1.5x)
Volume Average Length: Period for volume average (default: 20 bars)
Volatility Filter (ATR)
Volatility Filter: Enable/disable ATR confirmation
ATR Length: Calculation period (default: 14)
Min ATR vs Average: Required ATR level (default: 0.5x = 50%)
ORB Filters
ORB Breakout Distance Required: Toggle distance requirement
Min Breakout % Beyond ORB: Additional breakout threshold (default: 0.10%)
Session Filter
Trade Only During Best Hours: Enable time-based filtering
Session 1: First trading window (default: 0930-1130 EST)
Session 2: Second trading window (default: 1400-1530 EST)
Momentum Filter
Recent Momentum Required: Enable directional momentum check
Lookback Bars: Period for momentum comparison (default: 3 bars)
Daily Range Filter
Block Signals Near Daily Extremes: Prevent entries at extremes
Distance from High/Low %: Minimum distance required (default: 2.0%)
Candle Filter
Strong Directional Candle: Require candle strength
Min Candle Body %: Body-to-range ratio threshold (default: 60%)
Visual Signals
Show Entry Signals: Master toggle for visual signals
Show Arrows: Display entry arrows on chart
Background Color: Enable background highlighting
Best Practices
Start with default settings and adjust based on your trading style and asset volatility
Higher confluence requirements (5-6 timeframes) produce fewer but higher-quality signals
Enable all filters for conservative trading; disable some for more frequent signals
Use the dashboard as confirmation alongside your existing trading strategy
Backtest on your specific instruments before live trading
Consider market conditions—trending vs ranging markets may require different settings
Alerts
This indicator includes four alert conditions with JSON formatting for webhook integration:
Long Entry Signal: Triggers when all long conditions are met
Short Entry Signal: Triggers when all short conditions are met
Long Exit Signal: Triggers when opposing confluence reaches exit threshold
Short Exit Signal: Triggers when opposing confluence reaches exit threshold
Alert messages include ticker symbol, action (buy/sell), price, and quantity for automated trading systems.
Important Notes
This indicator works best on liquid instruments with clear price action
Highly volatile markets may require adjusted ATR and ORB distance settings
Session times are in EST timezone—adjust if trading non-US markets
The ORB calculation requires sufficient price history for the day
Signals are generated in real-time but should be confirmed at candle close
Limitations
Maximum of 6 timeframes can be analyzed due to TradingView's security call limits
ORB calculations may not work correctly on instruments with gaps or irregular sessions
The indicator is most effective during regular market hours when volume and volatility are adequate
Lower timeframes (1m, 5m) may produce more false signals in choppy conditions
License
Mozilla Public License 2.0 (MPL-2.0)
This indicator is licensed under the Mozilla Public License 2.0. You are free to use, modify, and distribute this code under the terms of the MPL-2.0. The full license text is available at mozilla.org
Key license provisions:
You may use this code commercially
You may modify and distribute modified versions
Modified versions must be released under the same license
You must include the original license notice in any distributions
No trademark rights are granted
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results. Trading involves substantial risk of loss. Always:
Practice proper risk management
Test thoroughly on paper/demo accounts before live trading
Use appropriate position sizing
Never risk more than you can afford to lose
Consult with a financial advisor for personalized advice
The creator assumes no liability for trading losses incurred using this indicator.
Version: 2.0
Pine Script Version: v6
Author: © EliasVictor
Moving Average Band StrategyOverview
The Moving Average Band Strategy is a fully customizable breakout and trend-continuation system designed for traders who need both simplicity and control.
The strategy creates adaptive bands around a user-selected moving average and executes trades when price breaks out of these bands, with advanced risk-management settings including optional Risk:Reward targets.
This script is suitable for intraday, swing, and positional traders across all markets — equities, futures, crypto, and forex.
Key Features
✔ Six Moving Average Types
Choose the MA that best matches your trading style:
SMA
EMA
WMA
HMA
VWMA
RMA
✔ Dynamic Bands
Upper Band built from MA of highs
Lower Band built from MA of lows
Adjustable band offset (%)
Color-coded band fill indicating price position
✔ Configurable Strategy Preferences
Toggle Long and/or Short trades
Toggle Risk:Reward Take-Profit
Adjustable Risk:Reward Ratio
Default position sizing: % of equity (configurable via strategy settings)
Entry Conditions
Long Entry
A long trade triggers when:
Price crosses above the Upper Band
Long trades are enabled
No existing long position is active
Short Entry
A short trade triggers when:
Price crosses below the Lower Band
Short trades are enabled
No existing short position is active
Clear entry markers and price labels appear on the chart.
Risk Management
This strategy includes a complete set of risk-controls:
Stop-Loss (Fixed at Entry)
Long SL: Lower Band
Short SL: Upper Band
These levels remain constant for the entire trade.
Optional Risk:Reward Take-Profit
Enabled/disabled using a toggle switch.
When enabled:
Long TP = Entry + (Risk × Risk:Reward Ratio)
Short TP = Entry – (Risk × Risk:Reward Ratio)
When disabled:
Exits are handled by reverse crossover signals.
Exit Conditions
Long Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Short Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Exit markers and price labels are plotted automatically.
Visual Tools
To improve clarity:
Upper & Lower Band (blue, adjustable width)
Middle Line
Dynamic band fill (green/red/yellow)
SL & TP line plotting when in position
Entry/Exit markers
Price labels for all executed trades
These are built to help users visually follow the strategy logic.
Alerts Included
Every trading event is covered:
Long Entry
Short Entry
Long SL / TP / Cross Exit
Short SL / TP / Cross Exit
Combined Alert for webhook/automation (JSON-formatted)
Perfect for algo trading, Discord bots, or automation platforms.
Best For
This strategy performs best in:
Trending markets
Breakout environments
High-momentum instruments
Clean intraday swings
Works seamlessly on:
Stocks
Index futures
Commodities
Crypto
Forex
⚠️ Important Disclaimer
This script is for educational purposes only.
Trading involves risk. Backtest results are not indicative of future performance.
Always validate settings and use proper position sizing.
BTCUSD – Market Structure Projection1. Short-Term Outlook
1. BTC is expected to complete a final liquidity sweep below recent lows.
2. A minor corrective rally into a premium zone offers a short opportunity.
3. Confirmation comes from rejection + RSI divergence.
2. Mid-Term Reversal Setup
4. After the sweep, BTC is projected to form a bullish break of structure (BOS).
5. A retest of demand provides the optimal long entry.
6. This phase begins the next expansion leg into 2026.
3. Long-Term Macro Trend
7. The higher-timeframe trend remains bullish despite local corrections.
8. BTC is expected to follow an impulse → correction → impulse pattern.
9. Macro upside targets remain positioned for new all-time highs.
4. Key Market Levels
Support Zones
10. $86,000 – $90,000 — primary liquidity-sweep region.
11. $92,500 – $94,000 — bullish retest confirmation zone.
Resistance Zones
12. $105,000 – $110,000 — mid-cycle rejection area.
13. $130,000 – $150,000 — macro expansion target range.
5. Trade Framework Summary
14. Short Setup: Enter after corrective rally into premium; target liquidity sweep.
15. Long Setup: Enter after BOS + demand retest; target macro continuation.
16. Structure favors a bullish expansion phase through 2026.
Volume Pressure and PercentVPP Volume Pressure and Percentage Indicator with a Volume Trendline that indicates which side is driving the flow.
Features:
1. Buy/Sell Pressure Bars (Core Volume Split)
The indicator separates each candle’s volume into buy volume (green) above the zero line and sell volume (red) below it. This gives you a real-time visualization of which side is more aggressive within the current bar. Instead of waiting for prices to move or candles to close, you can instantly see whether buyers or sellers are stepping in.
2. Dynamic Total Volume (Invisible Histogram + Status Line Color)
The total volume of each bar is tracked behind the scenes and displayed in the pinned status line using a dynamic color—green when buyers dominate, red when sellers dominate. The histogram for total volume is invisible to keep the chart clean, but the total volume figure stays visible and changes color based on who is in control. This gives you instant confirmation of whether institutional-sized volume supports the direction shown by the buy/sell pressure, which is especially valuable when evaluating the risk or conviction behind a potential entry.
3. Percentage Mode (% of Bar Volume)
When toggled on, the indicator converts each bar into percent buy vs percent sell, normalizing all flow to a 0–100% scale. This mode is incredibly useful when comparing pressure across different times of day, gaps, or varying volume conditions—such as early morning spikes versus lunchtime chop. By removing absolute volume from the equation, you gain a clean look at the actual imbalance between buyers and sellers.
4. 70% Pressure Band (Imbalance Threshold Zone)
In percentage mode, the indicator displays a subtle 70% band (a light gray zone) above and below the zero line, showing where buy or sell pressure reaches extreme dominance (≥70%). When a bar’s buy or sell percentage enters this zone, it highlights moments of exhaustion, acceleration, or potential reversal. The band acts like a real-time overbought/oversold gauge specifically for volume imbalance, not price.
5. Trend Line (Net Pressure Trend / Reversal Detector)
The trend line smooths out the net volume pressure (buy volume minus sell volume or its percentage equivalent) and shows the overall direction of order flow. When the line slopes upward, buyers are gaining control; when it slopes downward, sellers are taking over. This trend line acts as a real-time momentum indicator based directly on flow rather than price. Because it reacts quickly to intrabar shifts in buy/sell pressure, it often turns before price does—giving you a measurable timing edge.
6. Auto-Selecting Trend Source (Volume Net, Percent Net, or CVD)
The indicator lets you choose how the trend line is calculated: Volume Net (buy minus sell volume), Percent Net (normalized imbalance), or CVD (Cumulative Volume Delta) for long-term flow bias. The default “Auto” mode automatically switches between Volume Net and Percent Net depending on which view you’re using. This flexibility allows the trend line to remain meaningful whether you’re analyzing raw volume or normalized percentage data.
7. Pinned (Status Line) Totals in K/M/B Format
Regardless of whether you’re in volume or percentage mode, the indicator always displays Total Volume, Buy Volume, and Sell Volume in the status line using abbreviated K, M, B formatting. These values update in real time and are color-coded: green for bullish dominance, red for bearish. This gives you a concise snapshot of order flow strength on every bar.
---------------------
How To Use:
Support Level Zones
• Watch for Buy bars increasing + Trend line flipping up right at or slightly below support.
• This often signals absorption — market makers filling large buy orders before reversal.
• Confirmation: Price reclaims VWAP ... enter calls / longs.
Resistance Level Zones
• Watch for Sell bars increasing + Trend line flattening/turning down near resistance.
• This signals distribution or stop runs.
• Confirmation: Price rejects VWAP ... enter puts / shorts.
Breakout Traps
• Sometimes you’ll see price break a level, but the flow doesn’t confirm (buy volume doesn’t expand).
• That’s a false breakout — fade it with options opposite the move.
Range Oscillator Strategy + Stoch Confirm🔹 Short summary
This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades.
────────────────────────
0. Legal / risk disclaimer
────────────────────────
• This script is FREE and public. I do not charge any fee for it.
• It is for EDUCATIONAL PURPOSES ONLY.
• It is NOT financial advice and does NOT guarantee profits.
• Backtest results can be very different from live results.
• Markets change over time; past performance is NOT indicative of future performance.
• You are fully responsible for your own trades and risk.
Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital.
────────────────────────
1. About default settings and risk (very important)
────────────────────────
The script is configured with the following defaults in the `strategy()` declaration:
• `initial_capital = 10000`
→ This is only an EXAMPLE account size.
• `default_qty_type = strategy.percent_of_equity`
• `default_qty_value = 100`
→ This means 100% of equity per trade in the default properties.
→ This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade.
TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to:
1. Open **Strategy Settings → Properties**.
2. Set:
• Order size: **Percent of equity**
• Order size (percent): e.g. **1–2%** per trade
3. Make sure **commission** and **slippage** match your own broker conditions.
• By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets.
If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading.
────────────────────────
2. What this strategy tries to do (conceptual overview)
────────────────────────
This is a LONG-ONLY strategy designed to explore the combination of:
1. **Range Oscillator (Zeiierman-based)**
- Measures how far price has moved away from an adaptive mean.
- Uses an ATR-based range to normalize deviation.
- High positive oscillator values indicate strong price expansion away from the mean in a bullish direction.
2. **Stochastic as a timing filter**
- A classic Stochastic (%K and %D) is used.
- The logic requires %K to be below a user-defined level and then crossing above %D.
- This is intended to catch moments when momentum turns up again, rather than chasing every extreme.
3. **EMA Exit Filter (trend slope)**
- An EMA with configurable length (default 70) is calculated.
- The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition.
- This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted.
4. **Optional risk-management layer**
- Percentage-based Stop Loss and Take Profit (SL/TP).
- Risk/Reward (R-multiple) exit based on the distance from entry to SL.
- Implemented as OCO orders that work *on top* of the logical exits.
The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes.
────────────────────────
3. Components and how they work together
────────────────────────
(1) Range Oscillator (based on “Range Oscillator (Zeiierman)”)
• The script computes a weighted mean price and then measures how far price deviates from that mean.
• Deviation is normalized by an ATR-based range and expressed as an oscillator.
• When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction.
• When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled).
(2) Stochastic confirmation
• Classic Stochastic (%K and %D) is calculated.
• An entry requires:
- %K to be below a user-defined “Cross Level”, and
- then %K to cross above %D.
• This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point.
(3) EMA Exit Filter
• The EMA length is configurable via `emaLength` (default 70).
• The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA.
• If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition.
• This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit.
(4) Risk management (optional)
• Stop Loss (SL) and Take Profit (TP):
- Defined as percentages relative to average entry price.
- Both are disabled by default, but you can enable them in the Inputs.
• Risk/Reward Exit:
- Uses the distance from entry to SL to project a profit target at a configurable R-multiple.
- Also optional and disabled by default.
These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first.
────────────────────────
4. Entry & Exit logic (high level)
────────────────────────
A) Time filter
• You can choose a **Start Year** in the Inputs.
• Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`).
• This prevents accidental use of tiny cherry-picked windows and makes tests more honest.
B) Entry condition (long-only)
A long entry is allowed when ALL the following are true:
1. `timeCondition` is true (inside the backtest window).
2. If `useOscEntry` is true:
- Range Oscillator value must be above `entryLevel`.
3. If `useStochEntry` is true:
- Stochastic condition (`stochCondition`) must be true:
- %K < `crossLevel`, then %K crosses above %D.
If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`.
C) Exit condition (logical exits)
A position can be closed when:
1. `timeCondition` is true AND a long position is open, AND
2. At least one of the following is true:
- If `useOscExit` is true: Oscillator is below `exitLevel`.
- If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`).
In that case, `strategy.close("Long")` is called.
D) Risk-management exits
While a position is open:
• If SL or TP is enabled:
- `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages.
• If Risk/Reward exit is enabled:
- `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance.
These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels.
────────────────────────
5. Recommended backtest configuration (to avoid misleading results)
────────────────────────
To align with TradingView House Rules and avoid misleading backtests:
1. **Initial capital**
- 10 000 (or any value you personally want to work with).
2. **Order size**
- Type: **Percent of equity**
- Size: **1–2%** per trade is a reasonable starting point.
- Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice.
3. **Commission & slippage**
- Commission: around 0.1% if that matches your broker.
- Slippage: a few ticks (e.g. 3) to account for real fills.
4. **Timeframe & markets**
- Volatile symbols (e.g. crypto like BTCUSDT, or major indices).
- Timeframes: 1H / 4H / **1D (Daily)** are typical starting points.
- I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes.
5. **No “caution warning”**
- Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages.
- If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period.
────────────────────────
5a. About low trade count and rare signals
────────────────────────
This strategy is intentionally designed to trade RARELY:
• It is **long-only**.
• It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter).
• On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest.
TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case:
• The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window.
• The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals.
• Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data.
Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample.
────────────────────────
6. How to use this strategy (step-by-step)
────────────────────────
1. Add the script to your chart.
2. Open the **Inputs** tab:
- Set the backtest start year.
- Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter.
- Optionally enable SL, TP, and Risk/Reward exits.
3. Open the **Properties** tab:
- Set a realistic account size if you want.
- Set order size to a realistic % of equity (e.g. 1–2%).
- Confirm that commission and slippage are realistic for your broker.
4. Run the backtest:
- Look at Net Profit, Max Drawdown, number of trades, and equity curve.
- Remember that a low trade count means the statistics are not very strong.
5. Experiment:
- Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params.
- See how the metrics and trade frequency change.
6. Forward-test:
- Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time.
────────────────────────
7. Originality and usefulness (why this is more than a mashup)
────────────────────────
This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component:
• Range Oscillator:
- Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric.
• Stochastic:
- Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn.
• EMA Exit Filter:
- Trend-slope-based safety net to exit when the medium-term direction changes against the position.
• Risk module:
- Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic.
It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product.
────────────────────────
8. Limitations and good practices
────────────────────────
• No single strategy works on all markets or in all regimes.
• This script is long-only; it does not short the market.
• Performance can degrade when market structure changes.
• Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit.
Good practices:
- Test on multiple symbols and timeframes.
- Focus on stability and drawdown, not only on how high the profit line goes.
- View this as a learning tool and a basis for your own research.
────────────────────────
9. Licensing and credits
────────────────────────
• Core oscillator idea & base code:
- “Range Oscillator (Zeiierman)”
- © Zeiierman, licensed under CC BY-NC-SA 4.0.
• Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer:
- Modifications by jokiniemi.
Please respect both the original license and TradingView House Rules if you fork or republish any part of this script.
────────────────────────
10. No payments / no vendor pitch
────────────────────────
• This script is completely FREE to use on TradingView.
• There is no paid subscription, no external payment link, and no private signals group attached to it.
• If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice.
Use this script as a tool to learn, experiment, and build your own understanding of markets.
────────────────────────
11. Example backtest settings used in screenshots
────────────────────────
To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration:
• Symbol: BTCUSDT (or similar major BTC pair)
• Timeframe: 1D (Daily)
• Backtest period: from 2018 to the most recent data
• Initial capital: 10 000
• Order size type: Percent of equity
• Order size: 2% per trade
• Commission: 0.1%
• Slippage: 3 ticks
• Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default
• Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled
If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Put Option Profits inspired by Travis Wilkerson; SPX BacktesterPut Option Profits — Travis Wilkerson inspired. This tester evaluates a simple monthly SPX at-the-money credit-spread timing idea: enter on a fixed calendar rule (e.g., 1st Friday or 8th day with business-day shifting) at Open or Close, then exit exactly N calendar days later (first tradable day >= target, at Close). A trade is marked WIN if price at exit is above the entry price (1:1 risk proxy).
The book suggests forward testing 60-day and 180-day expirations to prove the concept. This tool lets you backtest both (and more) to see what actually works best. In the book, profits are taken when the spread reaches ~80% of max credit; losers are left to expire and cash-settle. This backtester does not model early profit-taking—every trade is held to the configured hold period and evaluated on price vs entry at the exit close. Think of it as a pure “set it and forget it” stress test. In live trading, you can still follow Travis’s 80% take-profit rule; TradingView just doesn’t simulate that here. Happy trading!
Features:
Schedule: Day-of-Month (with Prev/Next business-day shift, optional “stay in month”) or Nth Weekday (e.g., 1st Friday).
Entry timing: Open or Close.
Exit: N calendar days later at Close (holiday/weekend aware).
Filters: Optional EMA-200 “risk-on” filter.
Scope: Date range limiter.
Visuals: Entry/exit bubbles (paired colors) or simple win/loss dots.
Table: Overall Win% and N (within range).
Alerts: Entry alert (static condition + dynamic alert() message).
How to use:
[* ]Choose Start Mode (NthWeekday or DayOfMonth) and parameters (e.g., 1st Friday or DOM=8, PrevBizDay).
Pick Entry Timing (Open or Close).
Set Days In Trade (e.g., 150).
(Optional) Enable EMA filter and set Date Range.
Turn Bubbles on/off and/or Dots on/off.
Create alert:
Simple ping: Condition = this indicator -> Monthly Entry Signal -> “Once per bar” (Open) or “Once per bar close” (Close).
Rich message: Condition = this indicator -> Any alert() function call.
Notes:
Keep DOM shift in same month: when a DOM falls on a weekend/holiday, PrevBizDay/NextBizDay shift will stay inside the month if enabled; otherwise it can spill into the prior/next month. (Ignored for NthWeekday.)
Credits: Concept sparked by “Put Option Profits – How to turn ten minutes of free time into consistent cash flow each month” by Travis Wilkerson; this script is a neutral research tool (not financial advice).
W%R Pullback+EMA Trend [TS_Indie]🔰 Core Concept of the Strategy
The main idea is “Trend-Following with Momentum Pullback.”
This means trading in the direction of the main trend (defined by EMA) while using Williams %R to identify pullback entries (buying the dip or selling the rally) where momentum returns to the trend direction.
📊 Indicators Used
1. EMA Fast – Defines the short-term trend.
2. EMA Slow – Defines the long-term trend (used as a trend filter).
3. Williams %R
• Overbought zone: above -20
• Oversold zone: below -80
⚙️ Entry Rules
🔹 Buy Setup
1. EMA Fast > EMA Slow → Uptrend condition.
2. Williams %R on the previous candle dropped below -80, and on the current candle, it crosses back above -80 → indicates momentum returning to the upside.
3. Current close is above EMA Fast.
4. Entry Buy at the close of the candle where %R crosses above -80.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the lowest low between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
🔹 Sell Setup
1. EMA Fast < EMA Slow → Downtrend condition.
2. Williams %R on the previous candle went above -20, and on the current candle, it crosses back below -20 → indicates renewed selling momentum.
3. Current price is below EMA Fast.
4. Entry Sell at the close of the candle where %R crosses below -20.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the highest high between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
⚙️ Optional Parameters
• Custom Risk/Reward Ratio for Take Profit.
• Option to add ATR buffer to Stop Loss.
• Adjustable EMA Fast period.
• Adjustable EMA Slow period.
• Adjustable Williams %R period.
• Option to enable Long only / Short only positions.
• Customizable Backtest start and end date.
• Customizable trading session time.
⏰ Alert Function
Alerts display:
• Entry price
• Stop Loss price
• Take Profit price
Guys, try adjusting the parameters yourselves!
I’ve been tweaking the settings for several days and managed to get great results on XAU/USD in the 5-minute timeframe.
I think this strategy is quite interesting and could potentially deliver good results on other instruments as well.
⚠️ Disclaimer
This indicator is designed for educational and research purposes only.
It does not guarantee profits and should not be considered financial advice.
Trading in financial markets involves significant risk, including the potential loss of capital.
