Recherche dans les scripts pour "bear"
Noro's SILA v1.6L StrategyBacktesting
Backtesting (for all the time of existence of couple) only with software configurations to default (without optimization of parameters):
US = Uptrend-Sensivity
DS = Downtrend-Sensivity
It is recommended and by default:
- the normal market requires US=DS (for example US=5, DS=5)
- very bear market requires US DS, (for example US=5, DS=0)
- very bull market requires US DS, (US=0, DS=5)
Cryptocurrencies it is very bull market (US=0, DS=5)
Backtesting BTC/FIAT
D1 timeframe
identical parameters for all pairs
BTC/USD (Bitstamp) profit of +41805%
BTC/EUR (BTC-e) profit of +1147%
BTC/RUB (BTC-e) profit of +1162%
BTC/JPY (Bitflyer) profit of +215%
BTC/CNY (BTCChina) profit of 54948%
Backtesting ALTCOIN/BTC
D1 timeframe
identical parameters for all pairs
the exchange Poloniex
top-10 of cryptocurrencies on capitalization at the time of this text
NA = TradingView can't make backtest because of too low price of this cryptocurrency, or on the website there are no quotations of this cryptocurrency
ETH/BTC (Etherium) profit of +11690%
XRP/BTC (Ripple) loss of-100%
LTC/BTC (Litecoin) NA
ETC/BTC (Etherium Classic) profit of +214%
NEM/BTC loss of-49%
DASH/BTC profit of +106%
IOTA/BTC NA
XMR/BTC (Monero) profit of +96%
STRAT/BTC (Stratis) loss of-31%
ALTCOIN/ALTCOIN - not recomended
I don't need your money, I need reputation and likes.
daily wickedness dha betabeta of an idea to catch tops and exit before drops on BTC using a custom wickedness factor.
strategy is only going long.
The strategy starts with 2014 and ends 2017.
Its main purpose is to survive a bear market like 2014 only trading longpositions without taking too much damage.
Elder Ray (Bull Power) Strategy Backtest Developed by Dr Alexander Elder, the Elder-ray indicator measures buying
and selling pressure in the market. The Elder-ray is often used as part
of the Triple Screen trading system but may also be used on its own.
Dr Elder uses a 13-day exponential moving average (EMA) to indicate the
market consensus of value. Bull Power measures the ability of buyers to
drive prices above the consensus of value. Bear Power reflects the ability
of sellers to drive prices below the average consensus of value.
Bull Power is calculated by subtracting the 13-day EMA from the day's High.
Bear power subtracts the 13-day EMA from the day's Low.
You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect...
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
Everyday 0002 _ MAC 1st Trading Hour WalkoverThis is the second strategy for my Everyday project.
Like I wrote the last time - my goal is to create a new strategy everyday
for the rest of 2016 and post it here on TradingView.
I'm a complete beginner so this is my way of learning about coding strategies.
I'll give myself between 15 minutes and 2 hours to complete each creation.
This is basically a repetition of the first strategy I wrote - a Moving Average Crossover,
but I added a tiny thing.
I read that "Statistics have proven that the daily high or low is established within the first hour of trading on more than 70% of the time."
(source: )
My first Moving Average Crossover strategy, tested on VOLVB daily, got stoped out by the volatility
and because of this missed one nice bull run and a very nice bear run.
So I added this single line: if time("60", "1000-1600") regarding when to take exits:
if time("60", "1000-1600")
strategy.exit("Close Long", "Long", profit=2000, loss=500)
strategy.exit("Close Short", "Short", profit=2000, loss=500)
Sweden is UTC+2 so I guess UTC 1000 equals 12.00 in Stockholm. Not sure if this is correct, actually.
Anyway, I hope this means the strategy will only take exits based on price action which occur in the afternoon, when there is a higher probability of a lower volatility.
When I ran the new modified strategy on the same VOLVB daily it didn't get stoped out so easily.
On the other hand I'll have to test this on various stocks .
Reading and learning about how to properly test strategies is on my todo list - all tips on youtube videos or blogs
to read on this topic is very welcome!
Like I said the last time, I'm posting these strategies hoping to learn from the community - so any feedback, advice, or corrections is very much welcome and appreciated!
/pbergden
ARVEXV1“Failed Reversal – Opposite Candle Only (No Doji/Hammer/Hanging Man)”:
This strategy captures failed reversal attempts where the current candle is opposite to the previous candle and volume is higher. It enters long if a bearish candle fails to break a previous bullish candle’s low, and short if a bullish candle fails to break a previous bearish candle’s high. Signals are canceled for Doji, Hammer, or Hanging Man candles. Entries only, fully backtestable.
ARVEX V1“Failed Reversal – Opposite Candle Only (No Doji/Hammer/Hanging Man)”:
This strategy captures failed reversal attempts where the current candle is opposite to the previous candle and volume is higher. It enters long if a bearish candle fails to break a previous bullish candle’s low, and short if a bullish candle fails to break a previous bearish candle’s high. Signals are canceled for Doji, Hammer, or Hanging Man candles. Entries only, fully backtestable.
Long Bollinger Bands StrategyLong Bollinger Bands Strategy (XAUUSD) — Lower Band Reversal + 4-Step Scaling + Daily DD Guard
Long Bollinger Bands Strategy is a long-only Bollinger Bands reversal/mean-reversion strategy designed mainly for XAUUSD. It looks for a bearish push below the Lower Band followed by a bullish reclaim on candle close, then optionally scales in up to 4 entries (E1–E4) as price pulls back.
1) Risk Management & Position Sizing
The strategy includes a USD-based risk input: Risk per setup (USD).
It automatically calculates position size using the average SL distance across the 4-entry structure, then distributes size across entries with built-in weighting.
BackTest Lot checkbox:
OFF (default): uses normalized sizing (qty divided by 100)
ON: uses raw qty for backtesting workflows
2) SL/TP Management (Locked SL + Optional Range TP)
Stop Loss (SL): based on SL distance (pips from entry) from E1.
Take Profit (TP):
If TP (pips) > 0: fixed pip TP from E1
If TP (pips) = 0: TP is based on the signal candle range (high–low)
SL Lock: once the stop is tightened, it never loosens again (only moves in a protective direction) until the trade closes.
3) Daily Drawdown Protection
Tracks equity by day and stops opening new positions once Max daily drawdown (USD) is reached for that day.
4) Notes / Disclaimer
This strategy does not use volume, RSI, fundamentals, news filters, or session filters. Users should apply discretion and consider confirmations from other tools and market context. Results depend on symbol settings, spread, commission, and volatility regime. Always forward-test before using in live trading.
Designed for XAUUSD. The script uses an internal pip conversion (pipSize = 0.1) consistent with common gold quoting; verify your broker’s pip definition for best alignment.
5) Suggested Usage
Best used during volatile conditions or after a clear lower-band sweep and reclaim.
Consider pairing with trend filters or higher-timeframe bias.
6) Release Notes
Initial release: Long-only BB reclaim logic with 4-step scaling
Added: SL/TP lock logic and visual SL/TP lines
Added: Daily drawdown guard and backtest lot toggle OANDA:XAUUSD
Buy-Dip / Sell-Pullback Buy the Dip / Sell the Pullback – Trend-Following Strategy (EOD → Next Day Execution)
Overview
This is a trend-following futures strategy designed to participate in pullbacks within established trends, not to predict reversals.
It works on End-of-Day (EOD) confirmation and executes trades on the next trading session, making it suitable for positional and swing traders.
The strategy combines momentum, trend direction, volatility, and price location to filter for high-quality setups while avoiding overtrading.
🔍 Core Philosophy
Trade only in the direction of the prevailing trend
Buy dips in uptrends
Sell pullbacks in downtrends
Avoid chasing price after extended gaps
Use volatility-adjusted risk management (ATR-based SL & targets)
📊 Indicators Used
RSI (20)
Measures underlying momentum strength
Stochastic Oscillator (55, 34, 21)
Confirms pullback exhaustion within a trend
Supertrend (10, 2)
Defines primary trend direction
Bollinger Bands (20, 2)
Provides structural trend bias
ATR (5)
Used for:
Entry gap filter
Stop-loss
Profit target
Supertrend buffer
✅ Long (Buy) Setup – Evaluated at EOD
A long setup is generated when all of the following conditions are satisfied at the close of the trading day:
RSI(20) is above the bullish threshold (default: 48)
Stochastic %K is above %D (confirming pullback momentum)
Supertrend direction is bullish
Price is near or above Supertrend, allowing a volatility-adjusted buffer (ATR-based)
Price is above the Bollinger Band middle line
This combination ensures:
The market is trending up
Momentum supports continuation
The pullback is controlled, not a breakdown
❌ Short (Sell) Setup – Evaluated at EOD
A short setup is generated when:
RSI(20) is below the bearish threshold (default: 52)
Stochastic %K is below %D
Supertrend direction is bearish
Price is near or below Supertrend, with an ATR buffer
Price is below the Bollinger Band middle line
This filters for pullbacks within sustained downtrends.
⏰ Trade Execution Logic (Next Day Rule)
Once a setup is confirmed at EOD, a trade is attempted on the next trading session
To avoid chasing gaps:
Long trades are allowed only if price does not move more than a defined multiple of the previous day’s True Range
Short trades follow the same logic in reverse
This is implemented via limit orders, ensuring realistic backtesting and execution behavior
🛑 Risk Management
All exits are volatility-adjusted using ATR:
Stop-Loss:
1.1 × ATR(5) from entry price
Target:
2.2 × ATR(5) from entry price
This results in a risk–reward ratio of approximately 1:2
ATR is frozen at entry to avoid forward-looking bias.
🧠 Why This Strategy Works
Avoids low-quality trades during consolidation
Participates only when trend + momentum align
Prevents emotional gap-chasing
Adapts automatically to changing volatility
Suitable for index futures and liquid stocks
📌 Recommended Usage
Timeframe: Daily
Instruments:
Index Futures (e.g. NIFTY, BANKNIFTY)
Highly liquid stocks
Market Type: Trending markets
Not ideal for: Sideways or low-volatility environments
⚙️ Customization Tips
You can control trade frequency and aggressiveness by adjusting:
RSI thresholds
Supertrend buffer (ATR multiple)
Gap filter multiplier
Stochastic edge parameter
Looser settings → more trades
Stricter settings → higher selectivity
⚠️ Disclaimer
This strategy is for educational and research purposes only.
Backtest results do not guarantee future performance.
Always validate with paper trading before deploying real capital.
Quantum X StrategyQuantum X Strategy is a structured market-behavior based trading model developed for Midcap Nifty on the 15-minute timeframe.
It focuses on identifying directional strength, momentum alignment, and price participation using a multi-factor confirmation approach.
Rather than relying on a single indicator, the strategy evaluates multiple dimensions of price movement to determine whether the market environment is favorable for participation. This helps in avoiding random entries during low-quality or sideways conditions.
🔍 Conceptual Framework
The strategy dynamically observes:
Momentum expansion and contraction
Trend participation strength
Directional consistency over recent price action
Each market condition contributes to an internal decision process, allowing trades only when sufficient alignment is present. This approach helps filter out noise and improves trade selectivity.
📊 Trade Execution Philosophy
Trades are initiated only when market structure shows clear directional intent
Both bullish and bearish opportunities are evaluated independently
Positions are exited when momentum balance weakens or returns to a neutral state
No over-trading during indecisive phases
The system is designed to stay inactive during uncertain market conditions, which is a key part of its risk-aware behavior.
🕒 Backtesting Scope
For consistency and reliability, the strategy logic is activated only from January 2024 onward, ensuring analysis is focused on recent market behavior rather than outdated volatility patterns.
⚙️ Usage Guidelines
Instrument: MIDCAPNIFTY
Timeframe: 15 Minutes
Suitable for intraday and short-term positional observation
Works best when combined with disciplined risk management
⚠️ Disclaimer
This strategy is provided strictly for educational and research purposes.
Market conditions change, and past performance does not guarantee future results. Users should always forward-test and apply their own risk management before live use.
Optimized BTC Mean Reversion (RSI 20/65)📈 Optimized BTC Mean Reversion (RSI 20/65)
Optimized BTC Mean Reversion (RSI 20/65) is a rule-based trading strategy designed to capture mean-reversion moves in strong market structures, primarily optimized for Bitcoin, but adaptable to other liquid cryptocurrencies.
The strategy combines RSI extremes, Stochastic momentum, and EMA trend filtering to identify high-probability reversal zones while maintaining strict risk management.
🔍 Strategy Logic
This system focuses on entering trades when price temporarily deviates from equilibrium, while still respecting the broader trend.
✅ Long Conditions
RSI below 20 (oversold)
Stochastic below 25
Price trading above the 200 EMA (or within a controlled deviation)
Designed to buy sharp pullbacks in bullish conditions
❌ Short Conditions
RSI above 65 (overbought)
Stochastic above 75
Price trading below the 200 EMA
Designed to sell relief rallies in bearish conditions
🛡 Risk Management
Fixed Stop Loss: 4%
Fixed Take Profit: 6%
Risk/Reward: 1 : 1.5
No pyramiding (single position at a time)
Full equity position sizing (adjustable)
All exits are predefined at entry, ensuring consistency and emotional discipline.
📊 Indicators Used
200 EMA – Trend direction filter
RSI (14) – Mean-reversion trigger (20 / 65 levels)
Stochastic Oscillator – Momentum confirmation
👁 Visual Features
EMA plotted directly on chart
Real-time Stop Loss, Take Profit, and Entry Price lines
Clear long/short entry markers
Works on all timeframes (optimized for intraday and swing trading)
🔔 Alerts
Long entry alerts
Short entry alerts
(Perfect for automation or discretionary execution)
⚠️ Disclaimer
This strategy is intended for educational and research purposes only. Past performance does not guarantee future results. Always test on a demo account and adjust risk parameters to your own trading plan.
Scalping EMA + Pinbar Strategy (London & NY only, BE @ 1R)The scalping trading system uses two types of indicators:
EMA 10, EMA 21, EMA 50
Pinbar Indicator
Rules for entering a buy order:
If the closing price is above the EMA 50, the trend is uptrend and only buy orders should be considered.
The EMA 10 and EMA 21 lines must simultaneously be above the EMA 50.
The price must correct down at least 50% of the area created by the EMA 10 and EMA 21, or correct further down.
A Type 1 Pinbar candle (marked by the Pinbar indicator) must appear; this Pinbar candle must react to at least one of the three EMA lines (EMA 10, EMA 21, EMA 50) and close above the EMA 50.
This Pinbar candle must have a Pinbar strength value (marked by the Pinbar indicator) less than 2 to be considered valid. Check if the closing price of this pinbar candle is higher than the 50-day EMA and if the 10-day and 21-day EMAs are also higher than the 50-day EMA. If so, the conditions have been met and you can begin trading.
Place a buy stop order 0.1 pip higher than the highest price of the pinbar candle, and a stop loss order 0.1 pip lower than the lowest price of the pinbar candle. Set the take profit at 3R.
If the price moves past the previously set stop loss, cancel the pending order.
When the price moves 1R, move the stop loss back to the entry point.
The next trade can only be executed after the previous trade has moved the stop loss back to the entry point.
Rules for placing sell orders:
If the closing price is below the 50-day EMA, the trend is bearish, and only sell orders should be considered. The 10-day and 21-day EMAs must both be below the 50-day EMA.
The price must correct downwards by at least 50% of the area formed by the 10-day and 21-day EMAs, or even further.
A Type 1 pinbar candle (marked by the Pinbar indicator) must appear. This pinbar candle must react to at least one of the three EMAs (EMA 10, EMA 21, EMA 50) and close below the EMA 50.
This pinbar is valid if its strength (indicated by the Pinbar indicator) is less than 2. Verify that the closing price of this pinbar candle is below the EMA 50 and that both the EMA 10 and EMA 21 are below the EMA 50. If all conditions are met, the trade can be executed.
(This appears to be a separate entry rule and not part of the previous text.) Place a sell stop order 0.1 pip below the lowest point of the pinbar candle, and a stop loss order 0.1 pip above the highest point of the pinbar candle. Set the take profit point at 3R.
If the price moves past the previously set stop-loss point, cancel the pending order.
When the price moves 1R, move the stop-loss point back to the entry point.
The next trade can only be executed after the previous trade has moved the stop-loss point back to the entry point.
Capitulation Detector StrategyA multi-factor capitulation detector designed to identify exhaustion points in extended trends. It focuses on fading capitulation moves after multi-leg trends with extreme volume and price extension.
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THE CONCEPT
Capitulation occurs when the last holders give up — panic selling into lows or euphoric buying into highs. These moments create asymmetric opportunities because:
Sentiment becomes maximally skewed
Weak hands are flushed out
Price deviates far from equilibrium
The "fuel" for continuation is exhausted
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THE 6 FACTORS
Trend Persistence — Price stays on one side of 38 EMA for 12+ bars, confirming a sustained directional move
Acceleration — Price stays on one side of 5 EMA for 3+ bars, showing the move is accelerating into exhaustion
Volume Spike — Current bar volume ≥ 2x the 20-bar average
Body Expansion — Candle body ≥ 1.5x average, showing conviction/panic in the move
Extension — Price is 2+ ATR away from the 38 EMA, indicating overextension from equilibrium
Multi-Leg Structure — At least 3 consecutive lower lows (for longs) or higher highs (for shorts)
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SIGNAL LOGIC
Bullish Capitulation: 4+ factors align + price below 38 EMA + down candle + volume spike
Bearish Capitulation: 4+ factors align + price above 38 EMA + up candle + volume spike
The strategy enters counter-trend, fading the exhaustion move.
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EXIT OPTIONS
ATR-based stop loss (default: 2 ATR)
ATR-based take profit (default: 3 ATR)
Optional trailing stop
Time filter for session-specific trading
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BEST PRACTICES
Works best on liquid instruments with clean trends
More reliable after 3+ legs in the trend
Higher conviction when daily AND intraday timeframes align
"The bigger and more extended, the better"
Consider VWAP as additional confirmation (not coded here)
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SETTINGS GUIDE
Min Score: Increase for fewer, higher-quality signals
Volume Spike Multiplier: 2x; increase for stricter filter
Extension ATR: Higher values = more overextended setups only
Trend Bars Min: Higher values = longer established trends required
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ALERTS
Bullish Capitulation (potential long)
Bearish Capitulation (potential short)
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DISCLAIMER
This is a counter-trend strategy — inherently higher risk than trend-following. Always use proper position sizing and risk management. Backtest thoroughly on your specific instruments and timeframes.
Elliott Wave Full Fractal System v2.0Elliott Wave Full Fractal System v2.0 – Q.C. FINAL (Guaranteed R/R)
Elliott Wave Full Fractal System is a multi-timeframe wave engine that automatically labels Elliott impulses and ABC corrections, then builds a rule-based, ATR-driven risk/reward framework around the “W3–W4–W5” leg.
“Guaranteed R/R” here means every order is placed with a predefined stop-loss and take-profit that respect a minimum Reward:Risk ratio – it does not mean guaranteed profits.
Core Idea
This strategy turns a full fractal Elliott Wave labelling engine into a systematic trading model.
It scans fractal pivots on three wave degrees (Primary, Intermediate, Minor) to detect 5-wave impulses and ABC corrections.
A separate “Trading Degree” pivot stream, filtered by a 200-EMA trend filter and ATR-based dynamic pivots, is then used to find W4 pullback entries with a minimum, user-defined Reward:Risk ratio.
Default Properties & Risk Assumptions
The backtest uses realistic but conservative defaults:
// Default properties used for backtesting
strategy(
"Elliott Wave Full Fractal System - Q.C. FINAL (Guaranteed R/R)",
overlay = true,
initial_capital = 10000, // realistic account size
default_qty_type = strategy.percent_of_equity,
default_qty_value = 1, // 1% risk per trade
commission_type = strategy.commission.cash_per_contract,
commission_value = 0.005, // example stock commission
slippage = 0 // see notes below
)
Account size: 10,000 (can be changed to match your own account).
Position sizing: 1% of equity per trade to keep risk per idea sustainable and aligned with TradingView’s recommendations.
Commission: 0.005 cash per contract/share as a realistic example for stock trading.
Slippage: set to 0 in code for clarity of “pure logic” backtesting. Real-life trading will experience slippage, so users should adjust this according to their market and broker.
Always re-run the backtest after changing any of these values, and avoid using high risk fractions (5–10%+) as that is rarely sustainable.
1. Full Fractal Wave Engine
The script builds and maintains four pivot streams using ATR-adaptive fractals:
Primary Degree (Macro Trend):
Captures the large swings that define the major trend. Labels ①–⑤ and ⒶⒷⒸ using blue “Circle” labels and thicker lines.
Intermediate Degree (Trading Degree):
Captures the medium swings (swing-trading horizon). Uses teal labels ( (1)…(5), (A)(B)(C) ).
Minor Degree (Micro Structure):
Tracks short-term swings inside the larger waves. Uses red roman numerals (i…v, a b c).
ABC Corrections (Optional):
When enabled, the engine tries to detect standard A–B–C corrective structures that follow a completed 5-wave impulse and plots them with dashed lines.
Each degree uses a dynamic pivot lookback that expands when ATR is above its EMA, so the system naturally requires “stronger” pivots in volatile environments and reacts faster in quiet conditions.
2. Theory Rules & Strict Mode
Normal Mode: More permissive detection. Designed to show more wave structures for educational / exploratory use.
Strict Mode: Enforces key Elliott constraints:
Wave 3 not shorter than waves 1 and 5.
No invalid W4 overlap with W1 (for standard impulses).
ABC Logic: After a confirmed bullish impulse, the script expects a down-up-down corrective pattern (A,B,C). After a bearish impulse, it looks for up-down-up.
3. Trend Filter & Pivots
EMA Trend Filter: A configurable EMA (default 200) is used as a non-wave trend filter.
Price above EMA → Only long setups are considered.
Price below EMA → Only short setups are considered.
ATR-Adaptive Pivots: The pivot engine scales its left/right bars based on current ATR vs ATR EMA, making waves and trading pivots more robust in volatile regimes.
4. Dynamic Risk Management (Guaranteed R/R Engine)
The trading engine is designed around risk, not just pattern recognition:
ATR-Based Stop:
Stop-loss is placed at:
Entry ± ATR × Multiplier (user-configurable, default 2.0).
This anchors risk to current volatility.
Minimum Reward:Risk Ratio:
For each setup, the script:
Computes the distance from entry to stop (risk).
Projects a take-profit target at risk × min_rr_ratio away from entry.
Only accepts the setup if risk is positive and the required R:R ratio is achievable.
Result: Every order is created with both TP and SL at a predefined distance, so each trade starts with a known, minimum Reward:Risk profile by design.
“Guaranteed R/R” refers exclusively to this order placement logic (TP/SL geometry), not to win-rate or profitability.
5. Trading Logic – W3–W4–W5 Pattern
The Trading pivot stream (separate from visual wave degrees) looks for a simple but powerful pattern:
Bullish structure:
Sequence of pivots forms a higher-high / higher-low pattern.
Price is above the EMA trend filter.
A strong “W3” leg is confirmed with structure rules (optionally stricter in Strict mode).
Entry (Long – W4 Pullback):
The “height” of W3 is measured.
Entry is placed at a configurable Fibonacci pullback (default 50%) inside that leg.
ATR-based stop is placed below entry.
Take-profit is projected to satisfy min Reward:Risk.
Bearish structure:
Mirrored logic (lower highs/lows, price below EMA, W3 down, W4 retrace up, W5 continuation down).
Once a valid setup is found, the script draws a colored box around the entry zone and a label describing the type of signal (“LONG SETUP” or “SHORT SETUP”) with the suggested limit price.
6. Orders & Execution
Entry Orders: The strategy uses limit orders at the computed W4 level (“Sniper Long” or “Sniper Short”).
Exits: A single strategy.exit() is attached to each entry with:
Take-profit at the projected minimum R:R target.
Stop-loss at ATR-based level.
One Trade at a Time: New setups are only used when there is no open position (strategy.opentrades == 0) to keep the logic clear and risk contained.
7. Visual Guide on the Chart
Wave Labels:
Primary: ①,②,③,④,⑤, ⒶⒷⒸ
Intermediate: (1)…(5), (A)(B)(C)
Minor: i…v, a b c
Trend EMA: Single blue EMA showing the dominant trend.
Setup Boxes:
Green transparent box → long entry zone.
Red transparent box → short entry zone.
Labels: “LONG SETUP / SHORT SETUP” labels mark the proposed limit entry with price.
8. How to Use This Strategy
Attach the strategy to your chart
Choose your market (stocks, indices, FX, crypto, futures, etc.) and timeframe (for example 1h, 4h, or Daily). Then add the strategy to the chart from your Scripts list.
Start with the default settings
Leave all inputs on their defaults first. This lets you see the “intended” behaviour and the exact properties used for the published backtest (account size, 1% risk, commission, etc.).
Study the wave map
Zoom in and out and look at the three wave degrees:
Blue circles → Primary degree (big picture trend).
Teal (1)…(5) → Intermediate degree (swing structure).
Red i…v → Minor degree (micro waves).
Use this to understand how the engine is interpreting the Elliott structure on your symbol.
Watch for valid setups
Look for the coloured boxes and labels:
Green box + “LONG SETUP” label → potential W4 pullback long in an uptrend.
Red box + “SHORT SETUP” label → potential W4 pullback short in a downtrend.
Only trades in the direction of the EMA trend filter are allowed by the strategy.
Check the Reward:Risk of each idea
For each setup, inspect:
Limit entry price.
ATR-based stop level.
Projected take-profit level.
Make sure the minimum Reward:Risk ratio matches your own rules before you consider trading it.
Backtest and evaluate
Open the Strategy Tester:
Verify you have a decent sample size (ideally 100+ trades).
Check drawdowns, average trade, win-rate and R:R distribution.
Change markets and timeframes to see where the logic behaves best.
Adapt to your own risk profile
If you plan to use it live:
Set Initial Capital to your real account size.
Adjust default_qty_value to a risk level you are comfortable with (often 0.5–2% per trade).
Set commission and slippage to realistic broker values.
Re-run the backtest after every major change.
Use as a framework, not a signal machine
Treat this as a structured Elliott/R:R framework:
Filter signals by higher-timeframe trend, major S/R, volume, or fundamentals.
Optionally hide some wave degrees or ABC labels if you want a cleaner chart.
Combine the system’s structure with your own trade management and discretion.
Best Practices & Limitations
This is an approximate Elliott Wave engine based on fractal pivots. It does not replace a full discretionary Elliott analysis.
All wave counts are algorithmic and can differ from a manual analyst’s interpretation.
Like any backtest, results depend heavily on:
Symbol and timeframe.
Sample size (more trades are better).
Realistic commission/slippage settings.
The 0-slippage default is chosen only to show the “raw logic”. In real markets, slippage can significantly impact performance.
No strategy wins all the time. Losing streaks and drawdowns will still occur even with a strict R:R framework.
Disclaimer
This script is for educational and research purposes only and does not constitute financial advice or a recommendation to buy or sell any security. Past performance, whether real or simulated, is not indicative of future results. Always test on multiple symbols/timeframes, use conservative risk, and consult your financial advisor before trading live capital.
Trend Flow & Breakout Professional [Strategy]Description:
🌪️ Overview
Stop guessing. Start following the flow.
The Trend Flow & Breakout Professional is a high-precision visual trading system designed to solve the biggest problem traders face: Choppy Markets & Fakeouts.
Instead of relying on lagging indicators that generate false signals, this engine uses a proprietary "Momentum Alignment Algorithm" to identify when price action is entering a genuine expansion phase. It transforms complex trend data into a clean, easy-to-read visual roadmap, allowing you to catch the meat of the move while filtering out the noise.
🔮 Key Features
1. The "Traffic Light" Visual System Trading is 90% psychology. This script reduces mental fatigue by coloring the chart background to reflect the dominant market state:
🟢 Green Zone (Bullish Flow): Momentum is accelerating upwards. The system suggests holding long positions and ignoring minor pullbacks.
🔴 Red Zone (Bearish Flow): Structure has broken down. The system suggests defensive measures or short entries.
Note: The background remains active as long as the trend structure holds, preventing you from exiting trades too early.
2. Smart Noise Filtering Unlike standard crossover strategies that get destroyed in sideways ranges, this system includes a Multi-Layer Trend Filter. It only triggers a signal when:
Short-term momentum aligns perfectly with the medium-term direction.
Volatility expands significantly (breakout confirmation).
Price successfully clears key long-term structural resistance (The "Blue Sky" Zone).
3. Built-in "Smart Strategy" Backtester We have integrated a professional-grade position management module. You can customize how the strategy executes trades in the settings:
Mode A: Sniper (Trend Reversal): Enters heavily on the first confirmed breakout and holds until the trend reverses. Ideal for swing traders.
Mode B: Builder (Pyramiding): Adds to the position incrementally as the trend confirms its strength, maximizing profit during strong runs.
4. Cooldown Mechanism To prevent over-trading, the algorithm includes a smart "Cooldown Period" that prevents signal spamming during high-volatility consolidations.
⚙️ How to Trade This System
Wait for the Signal:
Look for the "Buy" / "Sell" labels accompanied by a bright Neon Candle.
Ensure the background color shifts (e.g., from Grey/Red to Green).
Ride the Zone:
Do not exit just because of one red candle. As long as the Background remains Green, the trend is healthy.
The background color acts as your "psychological anchor," helping you let profits run.
Exit / Reversal:
A complete background color flip (e.g., Green to Red) indicates a structural trend failure. This is your signal to close positions or flip directions.
⚠️ Disclaimer
This tool is for educational and technical analysis purposes only. Past performance does not guarantee future results. Always use proper risk management.
Alpha-Vector Unconstrained [GG_DOGE]
Alpha-Vector: Variance-Weighted Trend Capture Protocol
Authored by: GG_DOGE
Executive Summary
This algorithm represents the culmination of an exhaustive quantitative regression analysis, designed to exploit fat-tail distribution events in the SOL/USD cryptographic pair. By leveraging recursive historical data modeling on the 8-Hour timeframe, the strategy identifies high-probability momentum asymmetry—specifically isolating periods where directional volatility aligns with institutional order flow.
Unlike static heuristic models, this protocol utilizes a Dynamic Variance-Weighted Allocation Engine. This ensures that capital exposure is inversely correlated to market noise (entropy) while maximizing geometric compounding during high-conviction momentum phases. It essentially acts as a volatility filter, capitalizing on the statistical skew of the asset's return profile while enforcing rigorous drawdown mitigation via adaptive liquidity exits.
Key Algorithmic Features
Asymmetric Risk Architecture: The strategy deploys decoupled risk profiles for Long and Short vectors. Through backtest optimization, we have mathematically determined that bullish drift requires aggressive variance targeting, while bearish mean-reversion requires strictly constrained capital exposure to mitigate "short-squeeze" tail risks.
Volatility-Adjusted Position Sizing: Trade depth is not static. The algorithm calculates the instantaneous Average True Range (ATR) to normalize position size based on current market turbulence. This maintains a constant Risk-of-Ruin probability, regardless of price velocity.
Quantitatively Optimized Trend Filter: The entry signal is governed by a proprietary lookback period derived from computational brute-forcing of historical pivot points, designed to filter out Gaussian noise and only execute during significant structural market shifts.
Operational Guide (Strict Adherence Required)
This script comes pre-loaded with the statistically optimal parameters for the analyzed asset. No manual calibration is required.
Deployment Target:
Asset: CRYPTO:SOLUSD (Solana / US Dollar)
Timeframe: 8h (8-Hour Candle)
Exchange: Any major liquidity venue (Binance, Coinbase, Kraken, etc.)
Configuration:
Strategy Mode: Select "Long & Short" for the fully optimized protocol (captures upside momentum and hedges downside crashes).
Risk Parameters: The default values are mathematically tuned for maximum geometric growth (Highest PnL). Do not alter these unless you wish to artificially suppress the algorithm's volatility targeting.
Execution:
Capital Allocation: The logic is designed for compounding growth. It will automatically calculate the maximum lot size allowed based on your account equity, ensuring 100% capital efficiency without crossing into margin-call territory
Hash Ratings EngineHash Ratings Engine - Technical Consensus Strategy
A systematic trading strategy that harnesses TradingView's Technical Ratings to generate high-conviction entries with institutional-grade risk management.
What It Does
This strategy aggregates the consensus of 26+ technical indicators (RSI, MACD, Stochastics, multiple Moving Averages, etc.) into a single actionable signal. When enough indicators align bullish or bearish, the engine triggers an entry. Built-in trend filtering and ATR-based exits keep you on the right side of the market.
Key Features
Trend Filter - Only takes longs in uptrends, shorts in downtrends. This single filter typically improves results by 20-40% by avoiding counter-trend trades.
ATR-Based Risk Management - Stop loss and trailing stops adapt to current market volatility. Tight stops in calm markets, wider stops in volatile conditions.
Cooldown System - After a losing trade, the strategy waits before re-entering. This prevents the consecutive loss streaks that destroy accounts.
Clean Visuals - Fluorescent entry/exit signals with price level references. See exactly where you got in and out.
Settings Guide
Indicator Timeframe: Leave blank for current chart. Use higher timeframe for fewer, higher-quality signals.
Rating Source: "All" for balanced approach. "MAs" for trend-following. "Oscillators" for mean-reversion.
Entry Thresholds
Strong Signal Threshold: Higher = fewer trades but better conviction. Start at 0.5, test 0.4-0.6.
Risk Management
ATR Period: 12 is responsive, 14 is standard, 20+ is smoother.
Stop Loss: 2-3x ATR for tight stops, 3.5-4x for moderate, 5x+ for wide.
Trail Activation: How far price must move in profit before trailing begins.
Trail Offset: How closely the trail follows price.
Trend Filter
EMA Length: 150 works well on 4H charts. Use 100 for lower timeframes, 200 for daily.
Trade Timing
Cooldown: Keep enabled. 5 bars is a good starting point.
Best Practices
Start with default settings and backtest on your preferred instrument. Adjust the Strong Signal Threshold first - this has the biggest impact on trade frequency. Then tune the EMA length to match your timeframe. Finally, optimize the ATR multipliers for your risk tolerance.
Works on any liquid market - crypto, forex, stocks, futures. Higher timeframes (4H, Daily) tend to produce cleaner signals than lower timeframes.
Disclaimer
Past performance does not guarantee future results. Always backtest thoroughly and use proper position sizing. This strategy is for educational purposes - trade at your own risk.
Market Dynamics - Backtest Engine [NeuraAlgo]Market Dynamics – Backtest Engine
Market Dynamics – Backtest Engine is an advanced research-grade trading framework engineered by NeuraAlgo.
🔹 Core Engine – Dynamic Trend Model
The strategy leverages the NeuraAlgo – Market Dynamics indicator as its foundation, providing intelligent insights to guide trading decisions. It is designed to automatically identify the optimal settings for the NeuraAlgo – Market Dynamics indicator, helping traders fine-tune their strategy for maximum efficiency, accuracy, and profitability. This engine dynamically adapts to market conditions, ensuring your strategy stays optimized in real-time.
🔹 Optimization Engine
A built-in optimization module allows automatic testing of:
Winrate-focused configurations
Profit-focused configurations
Sensitivity ranges
Step sizes
Main Entry, Main Filter, Feature Filter, and Risk Manager categories
This enables rapid identification of optimal parameters similar to a lightweight AI optimizer.
This Backtesting + Auto Optimization Engine includes an integrated optimizer that automatically tests sensitivity ranges:
Maximize Winrate
Maximize Profits
Optimize Main Entries, Risk Manager, or Feature Filters
Users can set:
start sensitivity
step size
parameter category
The engine autonomously computes which parameter delivers the strongest performance.
🔹 How To Use
1. Identify the Parameters
First, you need to know which indicator parameters can be optimized. For the NeuraAlgo – Market Dynamics indicator, these might include:
Trend sensitivity
Smoothing periods
Threshold values for bullish/bearish signals
These parameters are the inputs your engine will test.
2. Define a Range
For each parameter, define a range of values to test. Example:
Sensitivity: 2 → 10
Trend period: 14 → 50
Threshold: 0.1 → 1.0
The more granular the range, the more precise the optimization—but it will also take longer.
3. Run Backtest Optimization
Attach the strategy to a chart.
Select optimization mode in your engine (or set the range for each parameter).
Start the backtest: the engine will simulate trades for every combination of parameter values.
The system will automatically record key metrics for each run:
Net profit
Win rate
Profit factor
Max drawdown
4. Analyze the Results
After the backtest, your engine will display a results table or chart showing performance for each parameter combination. Look for:
Highest net profit
Highest win rate
Or a combination depending on your strategy goals
Some engines will highlight the “best” parameter set automatically.
5. Apply Optimal Settings
Once identified:
Select the best-performing parameter values.
Apply them to your live strategy or paper trade.
Optionally, forward test to confirm they work on unseen market data.
Congratulations! The setup is now optimized.
🔹 Conclusion
The backtest optimization process helps you find the best parameter values for the NeuraAlgo – Market Dynamics indicator by systematically testing different settings and measuring their performance. By analyzing metrics like net profit, win rate, and drawdown, you can select optimized parameters that are more likely to perform consistently in real trading. Proper optimization ensures your strategy is data-driven, adaptable, and reduces guesswork, giving you a stronger edge in the market.
ChronoPulse MS-MACD Resonance StrategyChronoPulse MS-MACD Resonance Strategy
A systematic trading strategy that combines higher-timeframe market structure analysis with dual MACD momentum confirmation, ATR-based risk management, and real-time quality assurance monitoring.
Core Principles
The strategy operates on the principle of multi-timeframe confluence, requiring agreement between:
Market structure breaks (CHOCH/BOS) on a higher timeframe
Dual MACD momentum confirmation (classic and crypto-tuned profiles)
Trend alignment via directional EMAs
Volatility and volume filters
Quality score composite threshold
Strategy Components
Market Structure Engine : Detects Break of Structure (BOS) and Change of Character (CHOCH) events using confirmed pivots on a configurable higher timeframe. Default structure timeframe is 240 minutes (4H).
Dual MACD Fusion : Requires agreement between two MACD configurations:
Classic MACD: 12/26/9 (default)
Fusion MACD: 8/21/5 (default, optimized for crypto volatility)
Both must agree on direction before trade execution. This can be disabled to use single MACD confirmation.
Trend Alignment : Uses two EMAs for directional bias:
Directional EMA: 55 periods (default)
Execution Trend Guide: 34 periods (default)
Both must align with trade direction.
ATR Risk Management : All risk parameters are expressed in ATR multiples:
Stop Loss: 1.5 × ATR (default)
Take Profit: 3.0 × ATR (default)
Trail Activation: 1.0 × ATR profit required (default)
Trail Distance: 1.5 × ATR behind price (default)
Volume Surge Filter : Optional gate requiring current volume to exceed a multiple of the volume SMA. Default threshold is 1.4× the 20-period volume SMA.
Quality Score Gate : Composite score (0-1) combining:
Structure alignment (0.0-1.0)
Momentum strength (0.0-1.0)
Trend alignment (0.0-1.0)
ATR volatility score (0.0-1.0)
Volume intensity (0.0-1.0)
Default threshold: 0.62. Trades only execute when quality score exceeds this threshold.
Execution Discipline : Trade budgeting system:
Maximum trades per session: 6 (default)
Cooldown bars between entries: 5 (default)
Quality Assurance Console : Real-time monitoring panel displaying:
Structure status (pass/fail)
Momentum confirmation (pass/fail)
Volatility readiness (pass/fail)
Quality score (pass/fail)
Discipline compliance (pass/fail)
Performance metrics (win rate, profit factor)
Net PnL
Certification requires: Win Rate ≥ 40%, Profit Factor ≥ 1.4, Minimum 25 closed trades, and positive net profit.
Integrity Suite : Optional validation panel that audits:
Configuration sanity checks
ATR data readiness
EMA hierarchy validity
Performance realism checks
Strategy Settings
strategy(
title="ChronoPulse MS-MACD Resonance Strategy",
shorttitle="ChronPulse",
overlay=true,
max_labels_count=500,
max_lines_count=500,
initial_capital=100000,
currency=currency.USD,
pyramiding=0,
commission_type=strategy.commission.percent,
commission_value=0.015,
slippage=2,
default_qty_type=strategy.percent_of_equity,
default_qty_value=2.0,
calc_on_order_fills=true,
calc_on_every_tick=true,
process_orders_on_close=true
)
Key Input Parameters
Structure Timeframe : 240 (4H) - Higher timeframe for structure analysis
Structure Pivot Left/Right : 3/3 - Pivot confirmation periods
Structure Break Buffer : 0.15% - Buffer for structure break confirmation
MACD Fast/Slow/Signal : 12/26/9 - Classic MACD parameters
Fusion MACD Fast/Slow/Signal : 8/21/5 - Crypto-tuned MACD parameters
Directional EMA Length : 55 - Primary trend filter
Execution Trend Guide : 34 - Secondary trend filter
ATR Length : 14 - ATR calculation period
ATR Stop Multiplier : 1.5 - Stop loss in ATR units
ATR Target Multiplier : 3.0 - Take profit in ATR units
Trail Activation : 1.0 ATR - Profit required before trailing
Trail Distance : 1.5 ATR - Distance behind price
Volume Threshold : 1.4× - Volume surge multiplier
Quality Threshold : 0.62 - Minimum quality score (0-1)
Max Trades Per Session : 6 - Daily trade limit
Cooldown Bars : 5 - Bars between entries
Win-Rate Target : 40% - Minimum for QA certification
Profit Factor Target : 1.4 - Minimum for QA certification
Minimum Trades for QA : 25 - Required closed trades
Signal Generation Logic
A trade signal is generated when ALL of the following conditions are met:
Higher timeframe structure shows bullish (CHOCH/BOS) or bearish structure break
Both MACD profiles agree on direction (if fusion enabled)
Price is above both EMAs for longs (below for shorts)
ATR data is ready and above minimum threshold
Volume exceeds threshold × SMA (if volume gate enabled)
Quality score ≥ quality threshold
Trade budget available (under max trades per day)
Cooldown period satisfied
Risk Management
Stop loss and take profit are set immediately on entry
Trailing stop activates after 1.0 ATR of profit
Trailing stop maintains 1.5 ATR distance behind highest profit point
Position sizing uses 2% of equity per trade (default)
No pyramiding (single position per direction)
Limitations and Considerations
The strategy requires sufficient historical data for higher timeframe structure analysis
Quality gate may filter out many potential trades, reducing trade frequency
Performance metrics are based on historical backtesting and do not guarantee future results
Commission and slippage assumptions (0.015% + 2 ticks) may vary by broker
The strategy is optimized for trending markets with clear structure breaks
Choppy or ranging markets may produce false signals
Crypto markets may require different parameter tuning than traditional assets
Optimization Notes
The strategy includes several parameters that can be tuned for different market conditions:
Quality Threshold : Lower values (0.50-0.60) allow more trades but may reduce average quality. Higher values (0.70+) are more selective but may miss opportunities.
Structure Timeframe : Use 240 (4H) for intraday trading, Daily for swing trading, Weekly for position trading
Volume Gate : Disable for low-liquidity pairs or when volume data is unreliable
Dual MACD Fusion : Disable for mean-reverting markets where single MACD may be more responsive
Trade Discipline : Adjust max trades and cooldown based on your risk tolerance and market volatility
Non-Repainting Guarantee
All higher timeframe data requests use lookahead=barmerge.lookahead_off to prevent repainting. Pivot detection waits for full confirmation before registering structure breaks. All visual elements (tables, labels) update only on closed bars.
Alerts
Three alert conditions are available:
ChronoPulse Long Setup : Fires when all long entry conditions are met
ChronoPulse Short Setup : Fires when all short entry conditions are met
ChronoPulse QA Certification : Fires when Quality Assurance console reaches CERTIFIED status
Configure alerts with "Once Per Bar Close" delivery to match the non-repainting design.
Visual Elements
Structure Labels : CHOCH↑, CHOCH↓, BOS↑, BOS↓ markers on structure breaks
Directional EMA : Orange line showing trend bias
Trailing Stop Lines : Green (long) and red (short) trailing stop levels
Dashboard Panel : Real-time status display (structure, MACD, ATR, quality, PnL)
QA Console : Quality assurance monitoring panel
Integrity Suite Panel : Optional validation status display
Recommended Usage
Forward test with paper trading before live deployment
Monitor the QA console until it reaches CERTIFIED status
Adjust parameters based on your specific market and timeframe
Respect the trade discipline limits to avoid over-trading
Review quality scores and adjust threshold if needed
Use appropriate commission and slippage settings for your broker
Technical Implementation
The strategy uses Pine Script v6 with the following key features:
Multi-timeframe data requests with lookahead protection
Confirmed pivot detection for structure analysis
Dynamic trailing stop management
Real-time quality score calculation
Trade budgeting and cooldown enforcement
Comprehensive dashboard and monitoring panels
All source code is open and available for review and modification.
Disclaimer
This script is for educational and informational purposes only. It is not intended as financial, investment, or trading advice. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Always conduct your own research and consult with a qualified financial advisor before making any trading decisions. The author and TradingView are not responsible for any losses incurred from using this strategy.
VWolf - Raptor ClawOVERVIEW
The 'VWolf - Raptor Claw' is a straightforward scalping strategy designed for high-frequency trades based on the Stochastic RSI indicator. It focuses exclusively on identifying potential trend reversals through stochastic cross signals in extreme zones, without the need for additional confirmations. This makes it highly responsive to market movements, capturing rapid price shifts while maintaining simplicity.
This strategy is best suited for highly liquid and volatile markets like forex, indices, and major cryptocurrencies, where quick momentum shifts are common. It is ideal for experienced scalpers who prioritize fast entries and exits, but it can also be adapted for swing trading in lower timeframes.
Entry Conditions:
Long Entry:Stochastic RSI crosses above the oversold threshold (typically 20), indicating a potential bullish reversal.
Short Entry:Stochastic RSI crosses below the overbought threshold (typically 80), indicating a potential bearish reversal.
Exit Conditions:
Stop Loss: Set at the minimum (for longs) or maximum (for shorts) within a configurable lookback window to reduce risk.
Take Profit: Defined by a risk-reward ratio (RRR) input to optimize potential gains relative to risk.
CONCLUSION
The 'VWolf - Raptor Claw' strategy is perfect for traders seeking a simple yet aggressive approach to the markets. It capitalizes on sharp momentum shifts in extreme zones, relying on precise stop loss and take profit settings to capture rapid profits while minimizing risk. This approach is highly effective in high-volatility environments where quick decision-making is essential.
FOR MORE INFORMATION VISIT vwolftrading.com
Reversal WaveThis is the type of quantitative system that can get you hated on investment forums, now that the Random Walk Theory is back in fashion. The strategy has simple price action rules, zero over-optimization, and is validated by a historical record of nearly a century on both Gold and the S&P 500 index.
Recommended Markets
SPX (Weekly, Monthly)
SPY (Monthly)
Tesla (Weekly)
XAUUSD (Weekly, Monthly)
NVDA (Weekly, Monthly)
Meta (Weekly, Monthly)
GOOG (Weekly, Monthly)
MSFT (Weekly, Monthly)
AAPL (Weekly, Monthly)
System Rules and Parameters
Total capital: $10,000
We will use 10% of the total capital per trade
Commissions will be 0.1% per trade
Condition 1: Previous Bearish Candle (isPrevBearish) (the closing price was lower than the opening price).
Condition 2: Midpoint of the Body The script calculates the exact midpoint of the body of that previous bearish candle.
• Formula: (Previous Open + Previous Close) / 2.
Condition 3: 50% Recovery (longCondition) The current candle must be bullish (green) and, most importantly, its closing price must be above the midpoint calculated in the previous step.
Once these parameters are met, the system executes a long entry and calculates the exit parameters:
Stop Loss (SL): Placed at the low of the candle that generated the entry signal.
Take Profit (TP): Calculated by projecting the risk distance upward.
• Calculation: Entry Price + (Risk * 1).
Risk:Reward Ratio of 1:1.
About the Profit Factor
In my experience, TradingView calculates profits and losses based on the percentage of movement, which can cause returns to not match expectations. This doesn’t significantly affect trending systems, but it can impact systems with a high win rate and a well-defined risk-reward ratio. It only takes one large entry candle that triggers the SL to translate into a major drop in performance.
For example, you might see a system with a 60% win rate and a 1:1 risk-reward ratio generating losses, even though commissions are under control relative to the number of trades.
My recommendation is to manually calculate the performance of systems with a well-defined risk-reward ratio, assuming you will trade using a fixed amount per trade and limit losses to a fixed percentage.
Remember that, even if candles are larger or smaller in size, we can maintain a fixed loss percentage by using leverage (in cases of low volatility) or reducing the capital at risk (when volatility is high).
Implementing leverage or capital reduction based on volatility is something I haven’t been able to incorporate into the code, but it would undoubtedly improve the system’s performance dramatically, as it would fix a consistent loss percentage per trade, preventing losses from fluctuating with volatility swings.
For example, we can maintain a fixed loss percentage when volatility is low by using the following formula:
Leverage = % of SL you’re willing to risk / % volatility from entry point to exit or SL
And if volatility is high and exceeds the fixed percentage we want to expose per trade (if SL is hit), we could reduce the position size.
For example, imagine we only want to risk 15% per SL on Tesla, where volatility is high and would cause a 23.57% loss. In this case, we subtract 23.57% from 15% (the loss percentage we’re willing to accept per trade), then subtract the result from our usual position size.
23.57% - 15% = 8.57%
Suppose I use $200 per trade.
To calculate 8.57% of $200, simply multiply 200 by 8.57/100. This simple calculation shows that 8.57% equals about $17.14 of the $200. Then subtract that value from $200:
$200 - $17.14 = $182.86
In summary, if we reduced the position size to $182.86 (from the usual $200, where we’re willing to lose 15%), no matter whether Tesla moves up or down 23.57%, we would still only gain or lose 15% of the $200, thus respecting our risk management.
Final Notes
The code is extremely simple, and every step of its development is detailed within it.
If you liked this strategy, which complements very well with others I’ve already published, stay tuned. Best regards.
S&D Light+ Enhanced# S&D Light+ Enhanced - Supply & Demand Zone Trading Strategy
## 📊 Overview
**S&D Light+ Enhanced** is an advanced Supply and Demand zone identification and trading strategy that combines institutional order flow concepts with smart money techniques. This strategy automatically identifies high-probability reversal zones based on Break of Structure (BOS), momentum analysis, and first retest principles.
## 🎯 Key Features
### Smart Zone Detection
- **Automatic Supply & Demand Zone Identification** - Detects institutional zones where price is likely to react
- **Multi-Candle Momentum Analysis** - Validates zones with configurable momentum requirements
- **Break of Structure (BOS) Confirmation** - Ensures zones are created only after significant structure breaks
- **Quality Filters** - Minimum zone size and ATR-based filtering to eliminate weak zones
### Advanced Zone Management
- **Customizable Zone Display** - Choose between Geometric or Volume-Weighted midlines
- **First Retest Logic** - Option to trade only the first touch of each zone for higher probability setups
- **Zone Capacity Control** - Maintains a clean chart by limiting stored zones per type
- **Visual Zone Status** - Automatically marks consumed zones with faded midlines
### Risk Management
- **Dynamic Stop Loss** - Positioned beyond zone boundaries with adjustable buffer
- **Risk-Reward Ratio Control** - Customizable R:R for consistent risk management
- **Entry Spacing** - Minimum bars between signals prevents overtrading
- **Position Sizing** - Built-in percentage of equity allocation
## 🔧 How It Works
### Zone Creation Logic
**Supply Zones (Selling Pressure):**
1. Strong momentum downward movement (configurable body-to-range ratio)
2. Identified bullish base candle (where institutions accumulated shorts)
3. Break of Structure downward (price breaks below recent swing low)
4. Zone created at the base candle's high/low range
**Demand Zones (Buying Pressure):**
1. Strong momentum upward movement
2. Identified bearish base candle (where institutions accumulated longs)
3. Break of Structure upward (price breaks above recent swing high)
4. Zone created at the base candle's high/low range
### Entry Conditions
**Long Entry:**
- Price retests a demand zone (touches top of zone)
- Rejection confirmed (close above zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
**Short Entry:**
- Price retests a supply zone (touches bottom of zone)
- Rejection confirmed (close below zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
## ⚙️ Customizable Parameters
### Display Settings
- **Show Zones** - Toggle zone visualization on/off
- **Max Stored Zones** - Control number of active zones (1-50 per type)
- **Color Customization** - Adjust supply/demand colors and transparency
### Zone Quality Filters
- **Momentum Body Fraction** - Minimum body size for momentum candles (0.1-0.9)
- **Min Momentum Candles** - Number of consecutive momentum candles required (1-5)
- **Big Candle Body Fraction** - Alternative single-candle momentum threshold (0.5-0.95)
- **Min Zone Size %** - Minimum zone height as percentage of price (0.01-5.0%)
### BOS Configuration
- **Swing Length** - Lookback period for structure identification (3-20)
- **ATR Length** - Period for volatility measurement (1-50)
- **BOS Required Break** - ATR multiplier for valid structure break (0.1-3.0)
### Midline Options
- **None** - No midline displayed
- **Geometric** - Simple average of zone top/bottom
- **CenterVolume** - Volume-weighted center based on highest volume bar in window
### Risk Management
- **SL Buffer %** - Additional space beyond zone boundary (0-5%)
- **Take Profit RR** - Risk-reward ratio for target placement (0.5-10x)
### Entry Rules
- **Only 1st Retest per Zone** - Trade zones only once for higher quality
- **Min Bars Between Entries** - Prevent overtrading (1-20 bars)
## 📈 Recommended Settings
### Conservative (Lower Frequency, Higher Quality)
```
Momentum Body Fraction: 0.30
Min Momentum Candles: 2-3
BOS Required Break: 0.8-1.0
Min Zone Size: 0.15-0.20%
Only 1st Retest: Enabled
```
### Balanced (Default)
```
Momentum Body Fraction: 0.28
Min Momentum Candles: 2
BOS Required Break: 0.7
Min Zone Size: 0.12%
Only 1st Retest: Enabled
```
### Aggressive (Higher Frequency, More Signals)
```
Momentum Body Fraction: 0.20-0.25
Min Momentum Candles: 1-2
BOS Required Break: 0.4-0.5
Min Zone Size: 0.08-0.10%
Only 1st Retest: Disabled
```
## 🎨 Visual Elements
- **Red Boxes** - Supply zones (potential selling areas)
- **Green Boxes** - Demand zones (potential buying areas)
- **Dotted Midlines** - Center of each zone (fades when zone is used)
- **Debug Triangles** - Shows when zone creation conditions are met
- Red triangle down = Supply zone created
- Green triangle up = Demand zone created
## 📊 Best Practices
1. **Use on Higher Timeframes** - 1H, 4H, and Daily charts work best for institutional zones
2. **Combine with Trend** - Trade zones in direction of overall market structure
3. **Wait for Confirmation** - Don't enter immediately at zone touch; wait for rejection
4. **Adjust for Market Volatility** - Increase BOS multiplier in choppy markets
5. **Monitor Zone Quality** - Fresh zones typically have higher success rates
6. **Backtest Your Settings** - Optimize parameters for your specific market and timeframe
## ⚠️ Risk Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Always:
- Use proper position sizing
- Set appropriate stop losses
- Test thoroughly before live trading
- Consider market conditions and overall trend
- Never risk more than you can afford to lose
## 🔍 Data Window Information
The strategy provides real-time metrics visible in the data window:
- Supply Zones Count
- Demand Zones Count
- ATR Value
- Momentum Signals (Up/Down)
- BOS Signals (Up/Down)
## 📝 Version History
**v1.0 - Enhanced Edition**
- Improved BOS detection logic
- Extended base candle search range
- Added comprehensive input validation
- Enhanced visual feedback system
- Robust array bounds checking
- Debug signals for troubleshooting
## 💡 Tips for Optimization
- **Trending Markets**: Lower momentum requirements, tighter BOS filters
- **Ranging Markets**: Increase zone size minimum, enable first retest only
- **Volatile Assets**: Increase ATR multiplier and SL buffer
- **Lower Timeframes**: Reduce swing length, increase min bars between entries
- **Higher Timeframes**: Increase swing length, relax momentum requirements
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**Created with focus on institutional order flow, smart money concepts, and practical risk management.**
*Happy Trading! 📈*
Bollinger Bands Mean Reversion using RSI [Krishna Peri]How it Works
Long entries trigger when:
- RSI reaches oversold levels, and
- At least one bullish candle closes inside the lower Bollinger Band
Short entries trigger when:
- RSI reaches overbought levels, and
- At least one bearish candle closes inside the upper Bollinger Band
This approach aims to capture exhaustion moves where price pushes into extreme deviation from its mean and then snaps back toward the middle band.
Important Disclaimer
This is a mean-reversion strategy, which means it performs best in sideways, ranging, or slowly oscillating market conditions. When markets shift into strong trends, Bollinger Bands expand and volatility increases, which may cause some signals to become inaccurate or fail altogether.
For best results, combine this script with:
- Price action
- Market structure
- Higher-timeframe trend context
- Previous day/week/month highs & lows
- Untested liquidity levels or imbalance zones
- Session timing (Asia, London, NY)
Using these confluences helps filter out low-probability trades and significantly improves consistency and precision.
