BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
חפש סקריפטים עבור "entry"
Z-Score Trend Channels [BackQuant]Z-Score Trend Channels
A self-contained price-statistics framework that turns a rolling z-score into price channels, bias states, and trade markers. Run either trend-following or mean-reversion from the same tool with clear, on-chart context.
What it is
A rolling statistical map that measures how far price is from its recent average in standard-deviation units (z-score).
Adaptive channels drawn in price space from fixed z thresholds, so the rails breathe with volatility.
A simple trend proxy from z-score momentum to separate trending from ranging conditions.
On-chart signals for pullback entries, stretched extremes, and practical exits.
Core idea (plain English math)
Rolling mean and volatility - Over a lookback you get the average price and its standard deviation.
Z-score - How many standard deviations the current price is above or below its average: z = (price - mean) / stdev. z near 0 means near average; positive is above; negative is below.
Noise control - An EMA smooths the raw z to reduce jitter and false flickers.
Channels back in price - Fixed z levels are converted back to price to form the upper, lower, and extreme rails.
Trend proxy - A smoothed change in z is used as a lightweight trend-strength line. Positive strength with positive z favors uptrend; negative strength with negative z favors downtrend.
What you see on the chart
Channels and fills - Mean, upper, lower, and optional extreme lines. The area mean->upper tints with the bearish color, mean->lower tints with the bullish color.
Background tint (optional) - Soft green, red, or neutral based on detected trend state.
Signals - Bullish Entry (triangle up) when z exits the oversold zone upward; Bearish Entry (triangle down) when z exits the overbought zone downward; Extreme markers (diamonds) at the extreme bands with a one-bar turn.
Table - Current z, trend state, trend strength, distance to bands, market state tag, and a quick volatility regime label.
Edge labels - MEAN, OB, and OS labels slightly projected forward with level values.
Inputs you will actually use
Z-Score Period - Lookback for mean and stdev. Larger = slower and steadier rails, smaller = more reactive.
Smoothing Period - EMA on z. Lower = earlier but choppier flips; higher = later but cleaner.
Price Source - Default hlc3. Choose close if you prefer session-close logic.
Upper and Lower Thresholds - Default around +2.0 and -2.0. Tighten for more signals, widen for fewer and stronger.
Extreme Upper and Lower - Deeper stretch guards, e.g., +/- 2.5.
Strength Period - EMA on z momentum. Sets how fast the trend proxy flips.
Trend Threshold - Minimum absolute z to accept a directional bias.
Visual toggles - Channels, signals, background tint, stats table, colors, and optional last-bar trend label.
How to use it: trend-following playbook
Read the state - Uptrend when z > Trend Threshold and trend strength > 0. Downtrend when z < -Trend Threshold and trend strength < 0. Neutral otherwise.
Entries - In an uptrend, prefer Bullish Entry signals that fire near the lower channel. In a downtrend, prefer Bearish Entry signals that fire near the upper channel.
Stops - Conservative: beyond the extreme channel on your side. Tighter: just outside the standard band that framed the signal.
Exits - For longs, exit or trim on a cross back through z = 0 or a clean tag of the upper threshold. For shorts, mirror with z = 0 up-cross or tag of the lower threshold. You can also reduce if trend strength flips against you.
Adds - In strong trends, additional signals near your side’s band can be add points. Avoid adding once z hovers near the opposite band for several bars.
How to use it: mean-reversion playbook
Find stretch - Standard reversions: Bullish Entry when z leaves the oversold zone upward; Bearish Entry when z leaves the overbought zone downward. Aggressive reversions: Extreme markers at extreme bands with a one-bar turn.
Entries - Take the signal as price exits the zone. Prefer setups where trend strength is near zero or tilting against the prior push.
Targets - First target is the mean line. A runner can aim for the opposite standard channel if momentum keeps flipping.
Stops - Outside the extreme band beyond your entry. If fading without extremes, place risk just beyond the opposite standard band.
Filters - Optional: skip counter-trend fades against a very strong trend state unless your risk is tight and predefined.
Reading the stats table
Current Z-Score - Magnitude and sign of displacement now.
Trend State - Uptrend, Downtrend, or Ranging.
Trend Strength - Smoothed z momentum. Higher absolute values imply stronger directional conviction.
Distance to Upper/Lower - Percent distance from price to each band, useful for sizing targets or judging room left.
Market State - Overbought, Oversold, Extreme OB, Extreme OS, or Normal.
Volatility Regime - High, Normal, or Low relative to recent distribution. Expect bands to widen in High and tighten in Low.
Parameter guidance (conceptual)
Z-Score Period - Choose longer for a structural mean, shorter for a reactive mean.
Smoothing Period - Lower for earlier but noisier reads; higher for slower but steadier reads.
Thresholds - Start around +/- 2.0. Tighten for scalping or quiet ranges. Widen for noisy or fast markets.
Trend Threshold and Strength Period - Raise to avoid weak, transient bias. Lower to capture earlier regime shifts.
Practical examples
Trend pullback long - State shows Uptrend. Price tests the lower channel; z dips near or below the lower threshold; a Bullish Entry prints. Stop just below extreme lower; first target mean; keep a runner if trend strength stays positive.
Mean-revert short - State is Ranging. z tags the extreme upper, an Extreme Bearish marker prints, then a Bearish Entry prints on the leave. Stop above extreme upper; target the mean; consider a runner toward the lower channel if strength turns negative.
Potential Questions you might have
Why z-score instead of fixed offsets - Because the bands adapt with volatility. When the tape gets quiet the rails tighten, when it runs hot the rails expand. Your entries stay normalized.
Do I need both modes - No. Many users run only trend pullbacks or only mean-reversions. The tool lets you toggle what you need and keep the chart readable.
Multi-timeframe workflow - A common approach is to set bias from a higher timeframe’s trend state and execute on a lower timeframe’s signals that align with it.
Summary
Z-Score Trend Channels gives you an adaptive mean, volatility-aware rails, a simple trend lens, and clear signals. Trade the trend by buying pullbacks in green and selling pullbacks in red, or fade stretched extremes back to the mean with defined risk. One framework, two strategies, consistent logic.
Positional Toolbox v6 (distinct colors)what the lines mean (colors)
EMA20 (green) = fast trend
EMA50 (orange) = intermediate trend
EMA200 (purple, thicker) = primary trend
when the chart is “bullish” vs “bearish”
Bullish bias (look for buys):
EMA20 > EMA50 > EMA200 and EMA200 sloping up.
Bearish bias (avoid longs / consider exits):
EMA20 < EMA50 < EMA200 or price closing under EMA50/EMA200.
the two buy signals the script gives you
Pullback Long (triangle up)
Prints when price dips to EMA20 (green) and closes back above it while trend is bullish and ADX is decent.
Entry: buy on the same close or on a break of that candle’s high next day.
Stop: below the pullback swing-low (or below EMA50 for simplicity).
Best for: adding on an existing uptrend after a shallow dip.
Breakout 55D (“BO55” label)
Prints when price closes above prior 55-day high with volume surge in a bullish trend.
Entry: on the close that triggers, or next day above the breakout candle’s high.
Stop: below the breakout candle’s low (conservative: below base low).
Best for: fresh trend legs from bases.
simple “sell / exit” rules
Trend exit (clean & mechanical): exit if daily close < EMA50 (orange).
More conservative: only exit if close < EMA200 (purple).
Momentum fade / weak breakout: if BO55 triggers but price re-closes back inside the base within 1–3 sessions on above-avg volume → exit or cut size.
Profit taking: book some at +1.5R to +2R, trail the rest (e.g., below prior swing lows or EMA20).
quick visual checklist (what to look for)
Are the EMAs stacked up (green over orange over purple)? → ok to buy setups.
Did a triangle print near EMA20? → pullback long candidate.
Did a BO55 label print with strong volume? → breakout candidate.
Any close under EMA50 after you’re in? → reduce/exit.
timeframe
Use Daily for positional signals.
If you want a tighter entry, drop to 30m/1h only to time the trigger—but keep decisions anchored to the daily trend.
alerts to set (so you don’t miss signals)
Add alert on Breakout 55D and Pullback Long (from the indicator’s alertconditions).
Optional price alerts at the breakout level or EMA20 touch.
risk guardrails (MTF friendly)
Risk ≤1% of capital per trade.
Avoid fresh entries within ~5 trading days of earnings unless you accept gap risk.
Prefer high-liquidity NSE F&O names (your CSV watchlist covers this).
TL;DR (super short):
Green > Orange > Purple = uptrend.
Triangle near green = buy the pullback; stop under swing low/EMA50.
BO55 label = buy the breakout; stop under breakout candle/base.
Exit on close below EMA50 (or below EMA200 if you’re giving more room).
Opening Range IndicatorComplete Trading Guide: Opening Range Breakout Strategy
What Are Opening Ranges?
Opening ranges capture the high and low prices during the first few minutes of market open. These levels often act as key support and resistance throughout the trading day because:
Heavy volume occurs at market open as overnight orders execute
Institutional activity is concentrated during opening minutes
Price discovery happens as market participants react to overnight news
Psychological levels are established that traders watch all day
Understanding the Three Timeframes
OR5 (5-Minute Range: 9:30-9:35 AM)
Most sensitive - captures immediate market reaction
Quick signals but higher false breakout rate
Best for scalping and momentum trading
Use for early entry when conviction is high
OR15 (15-Minute Range: 9:30-9:45 AM)
Balanced approach - most popular among day traders
Moderate sensitivity with better reliability
Good for swing trades lasting several hours
Primary timeframe for most strategies
OR30 (30-Minute Range: 9:30-10:00 AM)
Most reliable but slower signals
Lower false breakout rate
Best for position trades and trend following
Use when looking for major moves
Core Trading Strategies
Strategy 1: Basic Breakout
Setup:
Wait for price to break above OR15 high or below OR15 low
Enter on the breakout candle close
Stop loss: Opposite side of the range
Target: 2-3x the range size
Example:
OR15 range: $100.00 - $102.00 (Range = $2.00)
Long entry: Break above $102.00
Stop loss: $99.50 (below OR15 low)
Target: $104.00+ (2x range size)
Strategy 2: Multiple Confirmation
Setup:
Wait for OR5 break first (early signal)
Confirm with OR15 break in same direction
Enter on OR15 confirmation
Stop: Below OR30 if available, or OR15 opposite level
Why it works:
Multiple timeframe confirmation reduces false signals and increases probability of sustained moves.
Strategy 3: Failed Breakout Reversal
Setup:
Price breaks OR15 level but fails to hold
Wait for re-entry into the range
Enter reversal trade toward opposite OR level
Stop: Recent breakout high/low
Target: Opposite side of range + extension
Key insight: Failed breakouts often lead to strong moves in the opposite direction.
Advanced Techniques
Range Quality Assessment
High-Quality Ranges (Trade these):
Range size: 0.5% - 2% of stock price
Clean boundaries (not choppy)
Volume spike during range formation
Clear rejection at range levels
Low-Quality Ranges (Avoid these):
Very narrow ranges (<0.3% of stock price)
Extremely wide ranges (>3% of stock price)
Choppy, overlapping candles
Low volume during formation
Volume Confirmation
For Breakouts:
Look for volume spike (2x+ average) on breakout
Declining volume often signals false breakout
Rising volume during range formation shows interest
Market Context Filters
Best Conditions:
Trending market days (SPY/QQQ with clear direction)
Earnings reactions or news-driven moves
High-volume stocks with good liquidity
Volatility above average (VIX considerations)
Avoid Trading When:
Extremely low volume days
Major economic announcements pending
Holidays or half-days
Choppy, sideways market conditions
Risk Management Rules
Position Sizing
Conservative: Risk 0.5% of account per trade
Moderate: Risk 1% of account per trade
Aggressive: Risk 2% maximum per trade
Stop Loss Placement
Inside the range: Quick exit but higher stop-out rate
Outside opposite level: More room but larger risk
ATR-based: 1.5-2x Average True Range below entry
Profit Taking
Target 1: 1x range size (take 50% off)
Target 2: 2x range size (take 25% off)
Runner: Trail remaining 25% with moving stops
Specific Entry Techniques
Breakout Entry Methods
Method 1: Immediate Entry
Enter as soon as price closes above/below range
Fastest entry but highest false signal rate
Best for strong momentum situations
Method 2: Pullback Entry
Wait for breakout, then pullback to range level
Enter when price bounces off former resistance/support
Better risk/reward but may miss some moves
Method 3: Volume Confirmation
Wait for breakout + volume spike
Enter after volume confirmation candle
Reduces false signals significantly
Multiple Timeframe Entries
Aggressive: OR5 break → immediate entry
Conservative: OR5 + OR15 + OR30 all align → enter
Balanced: OR15 break with OR30 support → enter
Common Mistakes to Avoid
1. Trading Poor-Quality Ranges
❌ Don't trade ranges that are too narrow or too wide
✅ Focus on clean, well-defined ranges with good volume
2. Ignoring Volume
❌ Don't chase breakouts without volume confirmation
✅ Always check for volume spike on breakouts
3. Over-Trading
❌ Don't force trades when ranges are unclear
✅ Wait for high-probability setups only
4. Poor Risk Management
❌ Don't risk more than planned or use tight stops in volatile conditions
✅ Stick to predetermined risk levels
5. Fighting the Trend
❌ Don't fade breakouts in strongly trending markets
✅ Align trades with overall market direction
Daily Trading Routine
Pre-Market (8:00-9:30 AM)
Check overnight news and earnings
Review major indices (SPY, QQQ, IWM)
Identify potential opening range candidates
Set alerts for range breakouts
Market Open (9:30-10:00 AM)
Watch opening range formation
Note volume and price action quality
Mark key levels on charts
Prepare for breakout signals
Trading Session (10:00 AM - 4:00 PM)
Execute breakout strategies
Manage existing positions
Trail stops as profits develop
Look for additional setups
Post-Market Review
Analyze winning and losing trades
Review range quality vs. outcomes
Identify improvement areas
Prepare for next session
Best Stocks/ETFs for Opening Range Trading
Large Cap Stocks (Best for beginners):
AAPL, MSFT, GOOGL, AMZN, TSLA
High liquidity, predictable behavior
Good range formation most days
ETFs (Consistent patterns):
SPY, QQQ, IWM, XLF, XLE
Excellent liquidity
Clear range boundaries
Mid-Cap Growth (Advanced traders):
Stocks with good volume (1M+ shares daily)
Recent news catalysts
Clean technical patterns
Performance Optimization
Track These Metrics:
Win rate by range type (OR5 vs OR15 vs OR30)
Average R/R (risk vs reward ratio)
Best performing market conditions
Time of day performance
Continuous Improvement:
Keep detailed trade journal
Review failed breakouts for patterns
Adjust position sizing based on win rate
Refine entry timing based on backtesting
Final Tips for Success
Start small - Paper trade or use tiny positions initially
Focus on quality - Better to miss trades than take bad ones
Stay disciplined - Stick to your rules even during losing streaks
Adapt to conditions - What works in trending markets may fail in choppy conditions
Keep learning - Markets evolve, so should your approach
The opening range strategy is powerful because it captures natural market behavior, but like all strategies, it requires practice, discipline, and proper risk management to be profitable long-term.
VWAP + Multi-Timeframe RSI StrategyThis strategy combines VWAP trend direction with confirmation from RSI on a higher timeframe. The idea is to only take trades when both intraday momentum and higher-timeframe trend are aligned, increasing accuracy.
LONG Entry:
Price above VWAP (bullish environment).
RSI on the current timeframe is below overbought (room to rise).
RSI on the higher timeframe (default H1) is above 50 (bullish confirmation).
SHORT Entry:
Price below VWAP (bearish environment).
RSI on the current timeframe is above oversold (room to fall).
RSI on the higher timeframe is below 50 (bearish confirmation).
Exit Rule:
Stop-loss near VWAP.
Take-profit at ~2x risk or when major levels are reached.
Best Timeframes:
Use 15m or 30m chart with H1 RSI for intraday trading.
Use 1H chart with Daily RSI for swing trading.
⚡ The higher-timeframe RSI filter reduces false signals and aligns trades with institutional flow.
Monday's Range Superpowerkyu🔔 Settings
You can customize the colors and toggle ON/OFF in the indicator settings.
Works on daily, hourly, and minute charts.
Easily visualize Monday’s high, low, and mid-line range.
📌 1. Support & Resistance with Monday’s Range
Monday High: Acts as the first resistance of the week.
◽ Example: If price breaks above Monday’s high after Tuesday, it signals potential bullish continuation → long setup.
Monday Low: Acts as the first support of the week.
◽ Example: If price breaks below Monday’s low, it signals bearish continuation → short setup.
📌 2. Mid-Line Trend Confirmation
Monday Mid-Line = average price of Monday.
Price above mid-line → bullish bias.
Price below mid-line → bearish bias.
Use mid-line breaks as entry confirmation for long/short positions.
📌 3. Breakout Strategy
Break of Monday’s High = bullish breakout → long entry.
Break of Monday’s Low = bearish breakout → short entry.
Place stop-loss inside Monday’s range for a conservative approach.
📌 4. False Breakout Strategy
If price breaks Monday’s high/low but then falls back inside Monday’s range, it is a False Breakout.
Strategy: Trade in the opposite direction.
◽ False Breakout at High → short.
◽ False Breakout at Low → long.
Stop-loss at the wick (extreme point) of the failed breakout.
📌 5. Range-Based Scalping
Use Monday’s high and low as a trading range.
Sell near Monday’s High, buy near Monday’s Low, repeat until breakout occurs.
📌 6. Weekly Volatility Forecast
Narrow Monday range → higher chance of strong trend later in the week.
Wide Monday range → lower volatility expected during the week.
📌 7. Pattern & Trend Analysis within Monday Range
Look for candlestick patterns around Monday’s High/Low/Mid-Line.
◽ Example: Double Top near Monday’s High = short setup.
◽ Repeated bounce at Mid-Line = strong long opportunity.
✅ Summary
The Monday’s Range (Superpowerkyu) Indicator helps traders:
Identify weekly support & resistance
Confirm trend direction with Mid-Line
Trade breakouts & false breakouts
Apply range scalping strategies
Forecast weekly volatility
⚡ Especially, the False Breakout strategy is powerful as it captures failed moves and sudden sentiment reversals.
Volume Profile Auto POC📌 Overview
Volume Profile Auto POC is a trend-following strategy that uses the automatically calculated Point of Control (POC) from the volume profile, combined with ATR zones, to capture reversals and breakouts.
By basing decisions on volume concentration, it dynamically visualizes the price levels most watched by market participants.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
Automatically detect the volume concentration area (POC) to improve entry accuracy
Optimize risk management through ATR-based volatility adjustment
Provide early and consistent signals when trends emerge
✨ Key Features
Automatic POC Detection : Updates the volume profile over a defined lookback window in real time
ATR Zone Integration : Defines a POC ± 0.5 ATR zone to clarify potential reversals/breakouts
Visual Support : Plots the POC line and zones on the chart for intuitive decision-making
📊 Trading Rules
Long Entry:
Price breaks above the POC + 0.5 ATR zone
Volume is above average to support the breakout
Short Entry:
Price breaks below the POC - 0.5 ATR zone
Volume is above average to support the downside move
Exit (or Reverse Position):
Price returns to the POC area
Or touches the ATR band
⚙️ Trading Parameters & Considerations
Indicator Name: Volume Profile Auto POC
Parameters:
Lookback Bars: 50
Bins for Volume Profile: 24
ATR Length: 14
ATR Multiplier: 2.0
🖼 Visual Support
POC line plotted in red
POC ± 0.5 ATR zone displayed as a semi-transparent box
ATR bands plotted in blue for confirmation
🔧 Strategy Improvements & Uniqueness
This strategy is inspired by traditional Volume Profile + ATR analysis,
while adding the improvement of a sliding-window mechanism for automatic POC updates.
Compared with conventional trend-following approaches,
its strength lies in combining both price and volume perspectives for decision-making.
✅ Summary
Volume Profile Auto POC automatically extracts key market levels (POC) and combines them with ATR-based zones,
providing a responsive trend-following method.
It balances clarity with practicality, aiming for both usability and reproducibility.
⚠️ This strategy is based on historical data and does not guarantee future profits.
Always use proper risk management when applying it.
FiniteStateMachine🟩 OVERVIEW
A flexible framework for creating, testing and implementing a Finite State Machine (FSM) in your script. FSMs use rules to control how states change in response to events.
This is the first Finite State Machine library on TradingView and it's quite a different way to think about your script's logic. Advantages of using this vs hardcoding all your logic include:
• Explicit logic : You can see all rules easily side-by-side.
• Validation : Tables show your rules and validation results right on the chart.
• Dual approach : Simple matrix for straightforward transitions; map implementation for concurrent scenarios. You can combine them for complex needs.
• Type safety : Shows how to use enums for robustness while maintaining string compatibility.
• Real-world examples : Includes both conceptual (traffic lights) and practical (trading strategy) demonstrations.
• Priority control : Explicit control over which rules take precedence when multiple conditions are met.
• Wildcard system : Flexible pattern matching for states and events.
The library seems complex, but it's not really. Your conditions, events, and their potential interactions are complex. The FSM makes them all explicit, which is some work. However, like all "good" pain in life, this is front-loaded, and *saves* pain later, in the form of unintended interactions and bugs that are very hard to find and fix.
🟩 SIMPLE FSM (MATRIX-BASED)
The simple FSM uses a matrix to define transition rules with the structure: state > event > state. We look up the current state, check if the event in that row matches, and if it does, output the resulting state.
Each row in the matrix defines one rule, and the first matching row, counting from the top down, is applied.
A limitation of this method is that you can supply only ONE event.
You can design layered rules using widlcards. Use an empty string "" or the special string "ANY" for any state or event wildcard.
The matrix FSM is foruse where you have clear, sequential state transitions triggered by single events. Think traffic lights, or any logic where only one thing can happen at a time.
The demo for this FSM is of traffic lights.
🟩 CONCURRENT FSM (MAP-BASED)
The map FSM uses a more complex structure where each state is a key in the map, and its value is an array of event rules. Each rule maps a named condition to an output (event or next state).
This FSM can handle multiple conditions simultaneously. Rules added first have higher priority.
Adding more rules to existing states combines the entries in the map (if you use the supplied helper function) rather than overwriting them.
This FSM is for more complex scenarios where multiple conditions can be true simultaneously, and you need to control which takes precedence. Like trading strategies, or any system with concurrent conditions.
The demo for this FSM is a trading strategy.
🟩 HOW TO USE
Pine Script libraries contain reusable code for importing into indicators. You do not need to copy any code out of here. Just import the library and call the function you want.
For example, for version 1 of this library, import it like this:
import SimpleCryptoLife/FiniteStateMachine/1
See the EXAMPLE USAGE sections within the library for examples of calling the functions.
For more information on libraries and incorporating them into your scripts, see the Libraries section of the Pine Script User Manual.
🟩 TECHNICAL IMPLEMENTATION
Both FSM implementations support wildcards using blank strings "" or the special string "ANY". Wildcards match in this priority order:
• Exact state + exact event match
• Exact state + empty event (event wildcard)
• Empty state + exact event (state wildcard)
• Empty state + empty event (full wildcard)
When multiple rules match the same state + event combination, the FIRST rule encountered takes priority. In the matrix FSM, this means row order determines priority. In the map FSM, it's the order you add rules to each state.
The library uses user-defined types for the map FSM:
• o_eventRule : Maps a condition name to an output
• o_eventRuleWrapper : Wraps an array of rules (since maps can't contain arrays directly)
Everything uses strings for maximum library compatibility, though the examples show how to use enums for type safety by converting them to strings.
Unlike normal maps where adding a duplicate key overwrites the value, this library's `m_addRuleToEventMap()` method *combines* rules, making it intuitive to build rule sets without breaking them.
🟩 VALIDATION & ERROR HANDLING
The library includes comprehensive validation functions that catch common FSM design errors:
Error detection:
• Empty next states
• Invalid states not in the states array
• Duplicate rules
• Conflicting transitions
• Unreachable states (no entry/exit rules)
Warning detection:
• Redundant wildcards
• Empty states/events (potential unintended wildcards)
• Duplicate conditions within states
You can display validation results in tables on the chart, with tooltips providing detailed explanations. The helper functions to display the tables are exported so you can call them from your own script.
🟩 PRACTICAL EXAMPLES
The library includes four comprehensive demos:
Traffic Light Demo (Simple FSM) : Uses the matrix FSM to cycle through traffic light states (red → red+amber → green → amber → red) with timer events. Includes pseudo-random "break" events and repair logic to demonstrate wildcards and priority handling.
Trading Strategy Demo (Concurrent FSM) : Implements a realistic long-only trading strategy using BOTH FSM types:
• Map FSM converts multiple technical conditions (EMA crosses, gaps, fractals, RSI) into prioritised events
• Matrix FSM handles state transitions (idle → setup → entry → position → exit → re-entry)
• Includes position management, stop losses, and re-entry logic
Error Demonstrations : Both FSM types include error demos with intentionally malformed rules to showcase the validation system's capabilities.
🟩 BRING ON THE FUNCTIONS
f_printFSMMatrix(_mat_rules, _a_states, _tablePosition)
Prints a table of states and rules to the specified position on the chart. Works only with the matrix-based FSM.
Parameters:
_mat_rules (matrix)
_a_states (array)
_tablePosition (simple string)
Returns: The table of states and rules.
method m_loadMatrixRulesFromText(_mat_rules, _rulesText)
Loads rules into a rules matrix from a multiline string where each line is of the form "current state | event | next state" (ignores empty lines and trims whitespace).
This is the most human-readable way to define rules because it's a visually aligned, table-like format.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_rulesText (string)
Returns: No explicit return. The matrix is modified as a side-effect.
method m_addRuleToMatrix(_mat_rules, _currentState, _event, _nextState)
Adds a single rule to the rules matrix. This can also be quite readble if you use short variable names and careful spacing.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_currentState (string)
_event (string)
_nextState (string)
Returns: No explicit return. The matrix is modified as a side-effect.
method m_validateRulesMatrix(_mat_rules, _a_states, _showTable, _tablePosition)
Validates a rules matrix and a states array to check that they are well formed. Works only with the matrix-based FSM.
Checks: matrix has exactly 3 columns; no empty next states; all states defined in array; no duplicate states; no duplicate rules; all states have entry/exit rules; no conflicting transitions; no redundant wildcards. To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the rules and states are ready.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_a_states (array)
_showTable (bool)
_tablePosition (simple string)
Returns: `true` if the rules and states are valid; `false` if errors or warnings exist.
method m_getStateFromMatrix(_mat_rules, _currentState, _event, _strictInput, _strictTransitions)
Returns the next state based on the current state and event, or `na` if no matching transition is found. Empty (not na) entries are treated as wildcards if `strictInput` is false.
Priority: exact match > event wildcard > state wildcard > full wildcard.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_currentState (string)
_event (string)
_strictInput (bool)
_strictTransitions (bool)
Returns: The next state or `na`.
method m_addRuleToEventMap(_map_eventRules, _state, _condName, _output)
Adds a single event rule to the event rules map. If the state key already exists, appends the new rule to the existing array (if different). If the state key doesn't exist, creates a new entry.
Namespace types: map
Parameters:
_map_eventRules (map)
_state (string)
_condName (string)
_output (string)
Returns: No explicit return. The map is modified as a side-effect.
method m_addEventRulesToMapFromText(_map_eventRules, _configText)
Loads event rules from a multiline text string into a map structure.
Format: "state | condName > output | condName > output | ..." . Pairs are ordered by priority. You can have multiple rules on the same line for one state.
Supports wildcards: Use an empty string ("") or the special string "ANY" for state or condName to create wildcard rules.
Examples: " | condName > output" (state wildcard), "state | > output" (condition wildcard), " | > output" (full wildcard).
Splits lines by \n, extracts state as key, creates/appends to array with new o_eventRule(condName, output).
Call once, e.g., on barstate.isfirst for best performance.
Namespace types: map
Parameters:
_map_eventRules (map)
_configText (string)
Returns: No explicit return. The map is modified as a side-effect.
f_printFSMMap(_map_eventRules, _a_states, _tablePosition)
Prints a table of map-based event rules to the specified position on the chart.
Parameters:
_map_eventRules (map)
_a_states (array)
_tablePosition (simple string)
Returns: The table of map-based event rules.
method m_validateEventRulesMap(_map_eventRules, _a_states, _a_validEvents, _showTable, _tablePosition)
Validates an event rules map to check that it's well formed.
Checks: map is not empty; wrappers contain non-empty arrays; no duplicate condition names per state; no empty fields in o_eventRule objects; optionally validates outputs against matrix events.
NOTE: Both "" and "ANY" are treated identically as wildcards for both states and conditions.
To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the map is ready.
Namespace types: map
Parameters:
_map_eventRules (map)
_a_states (array)
_a_validEvents (array)
_showTable (bool)
_tablePosition (simple string)
Returns: `true` if the event rules map is valid; `false` if errors or warnings exist.
method m_getEventFromConditionsMap(_currentState, _a_activeConditions, _map_eventRules)
Returns a single event or state string based on the current state and active conditions.
Uses a map of event rules where rules are pre-sorted by implicit priority via load order.
Supports wildcards using empty string ("") or "ANY" for flexible rule matching.
Priority: exact match > condition wildcard > state wildcard > full wildcard.
Namespace types: series string, simple string, input string, const string
Parameters:
_currentState (string)
_a_activeConditions (array)
_map_eventRules (map)
Returns: The output string (event or state) for the first matching condition, or na if no match found.
o_eventRule
o_eventRule defines a condition-to-output mapping for the concurrent FSM.
Fields:
condName (series string) : The name of the condition to check.
output (series string) : The output (event or state) when the condition is true.
o_eventRuleWrapper
o_eventRuleWrapper wraps an array of o_eventRule for use as map values (maps cannot contain collections directly).
Fields:
a_rules (array) : Array of o_eventRule objects for a specific state.
TURT Donchian Ladder v3.13How to trade TURT+ with the v3.13 script
1) Pick the system & arm the entry
• In the script, choose System = S1 (20D) or S2 (55D).
The HUD always shows both rails for reference, but the ladder (Entry/+Adds) uses the system you pick.
• Your Entry is shown as Pivot + 0.1×N (rounded).
• Place a stop-limit “parent” order at that Entry price. (Classic Turtle uses an entry stop; I suggest a tight limit offset so you don’t chase a blow-through.)
• Initial stop = N2 = Entry − 2×N (rounded). Put that in immediately.
If you like only confirming on a bar close, leave confirmClose = true and place the parent after the close that breaks out. If you want intrabar fills, set confirmClose = false and keep the stop-limit active intraday.
2) Size it the way you planned
• Set acctEquity / riskCapPct / posCapUSD / entryFrac / entryRiskFrac / sizingMode.
• HUD gives Rec Entry Qty (when flat) and, once in, it shows:
• Next Rung (price)
• Suggested AddShares (honors RiskCap & PosCap)
• Proj Stop if Add (ratcheted N2)
• A limiter note (RiskCap or PosCap) if you’re constrained.
3) After entry fills, stage the ADDs (only at fixed +N steps)
• Adds are NOT “every Donchian break.” You add only at:
• Add-1 = Entry + 0.5×N
• Add-2 = Entry + 1.0×N
• Add-3 = Entry + 1.5×N (optional)
• Use the HUD’s Suggested AddShares for each rung (it respects your RiskCap/PosCap).
• Place stop-limit orders for each add (either immediately as a contingent OTO chain that arms only after Entry fills, or you arm each add when price approaches—your choice).
• On each add fill, ratchet the catastrophic stop for the entire position to Last-Add − 2×N (the script and HUD show Proj Stop if Add so you know where it will land). Never move it lower.
Pro tip: If your broker supports OTO/OTOCO:
• OTO parent = Entry stop-limit.
• On fill, fire an OCO with the N2 stop (no target), and also stage child stop-limits for Add-1 / Add-2 / Add-3 with the correct sizes. If your broker can’t chain that deep, just use the script’s alerts (Entry/Add-1/Add-2/Add-3/Exits) to place/adjust orders quickly.
4) Exits (two layers)
• Catastrophic (always on): the N2 stop you’re ratcheting (Last-Add − 2×N).
• Trend exits (runner):
• S1: 10-low close (HUD shows it).
• S2: 20-low close (HUD shows it).
• Profit-taking (optional): sell ~50% at +2.5R to +3R vs current N2; let the runner trail with 10-low/20-low. You can keep N2 as a hard backstop.
5) Should you pre-set everything or buy live?
Both work; pick the style that fits you:
Preset (Turtle-pure, rules-based)
• ✅ You won’t miss the breakout; minimal discretion.
• ✅ Broker handles fills even if you’re away.
• ⚠️ You may get the occasional intraday “poke” (use confirmClose + place after close if you want fewer).
Buy on break manually
• ✅ Lets you check tape/volume or any extra gates before clicking.
• ⚠️ Higher chance of slippage or of simply missing the trigger.
A nice hybrid: place the Entry order, then arm Add-1/2/3 when price is nearing each rung and the HUD shows Suggested AddShares > 0 (green risk read).
⸻
6) Quick checklist per trade
1. System: S1 or S2?
2. Levels: Entry / Add-1 / Add-2 / Add-3 / 10-low / 20-low / N2 (rounded).
3. Sizing: confirm RiskCap/PosCap; HUD shows Suggested AddShares and limiter.
4. Orders:
• Parent Entry stop-limit.
• N2 stop (rounded).
• Stage adds (stop-limits) with sizes from HUD.
5. On fill: ratchet stop to Last-Add − 2×N; adjust remaining adds and sizes.
⸻
7) Example with your MU position (pattern)
• You’re already in: set entryQty and entryPman in the inputs to match your fill.
• HUD now focuses on Next Rung, Suggested AddShares, and Proj Stop if Add.
• If Suggested AddShares = 0 and limiter says RiskCap or PosCap, you’ll still see the next rung price and Proj Stop if Add so you can decide whether to override.
⸻
Bottom line
• Entry: buy the Donchian breakout + 0.1N with a stop-limit (Turtle style).
• Adds: only at +0.5N steps, sized by HUD; not on every future Donchian break.
• Stops: keep (and ratchet) the N2 catastrophic; trail runner on 10-low / 20-low.
If you want, tell me your broker/platform and I’ll map this to exact order ticket types (stop-limit/OTO/OCO) and a tiny checklist you can keep next to your screen.
Position Size CalculatorPosition Size Calculator
This open-source Pine Script® indicator helps traders manage risk by calculating position size, margin, and risk/reward based on account size, leverage, entry, stop-loss, and take-profit. It features a customizable table and optional chart lines/labels for clear trade planning across stocks, forex, crypto, and futures.
What It Does
- Position Size: Computes units to trade based on risk percentage and stop-loss distance, capped by leverage.
- Margin: Calculates initial margin in base currency and USD, with account size percentage.
- Risk/Reward: Shows risk-reward ratio, percentage price movements, and USD gains/losses.
- Visualization: Displays results in a table and optional chart lines/labels with customizable styles.
How It Works
- Precision: Adjusts price formatting using syminfo.mintick for accuracy across assets.
- Calculations: Position size = accountSize * (riskPercent / 100) / |entry - stoploss|, capped by accountSize * leverage / entry. Margin = positionSize / leverage. Risk-reward = |takeprofit - entry| / |stoploss - entry|.
- Display: Table shows metrics; optional lines/labels plot entry, stop-loss, and take-profit with percentage and USD details.
How to Use
- Set Inputs:
1- Account Size (USD): Your capital (e.g., 1000).
2- % Risk per Trade: Risk tolerance (e.g., 1%).
3- Leverage: Broker leverage (e.g., 1x, 10x).
4- Entry, Stop Loss, Take Profit: Trade prices.
5- Show Lines and Labels: Enable chart overlays.
- Customize: Adjust table position, colors, and line styles (Solid, Dashed, Dotted).
- View Results: Table shows position size, margin, and risk/reward. Chart lines/labels (if enabled) display prices, percentages, and USD outcomes.
- Apply: Use metrics for trade execution; modify code for custom features.
Notes
- Ensure valid inputs (entry ≠ stop-loss, both positive) to avoid “N/A”.
- Open-source: Inspect or extend the code for your needs.
- Contact the author via TradingView for feedback.
HawkEye EMA Cloud
# HawkEye EMA Cloud - Enhanced Multi-Timeframe EMA Analysis
## Overview
The HawkEye EMA Cloud is an advanced technical analysis indicator that visualizes multiple Exponential Moving Average (EMA) relationships through dynamic color-coded cloud formations. This enhanced version builds upon the original Ripster EMA Clouds concept with full customization capabilities.
## Credits
**Original Author:** Ripster47 (Ripster EMA Clouds)
**Enhanced Version:** HawkEye EMA Cloud with advanced customization features
## Key Features
### 🎨 **Full Color Customization**
- Individual bullish and bearish colors for each of the 5 EMA clouds
- Customizable rising and falling colors for EMA lines
- Adjustable opacity levels (0-100%) for each cloud independently
### 📊 **Multi-Layer EMA Analysis**
- **5 Configurable EMA Cloud Pairs:**
- Cloud 1: 8/9 EMAs (default)
- Cloud 2: 5/12 EMAs (default)
- Cloud 3: 34/50 EMAs (default)
- Cloud 4: 72/89 EMAs (default)
- Cloud 5: 180/200 EMAs (default)
### ⚙️ **Advanced Customization Options**
- Toggle individual clouds on/off
- Adjustable EMA periods for all timeframes
- Optional EMA line display with color coding
- Leading period offset for cloud projection
- Choice between EMA and SMA calculations
- Configurable source data (HL2, Close, Open, etc.)
## How It Works
### Cloud Formation
Each cloud is formed by the area between two EMAs of different periods. The cloud color dynamically changes based on:
- **Bullish (Green/Custom):** When the shorter EMA is above the longer EMA
- **Bearish (Red/Custom):** When the shorter EMA is below the longer EMA
### Multiple Timeframe Analysis
The indicator provides a comprehensive view of trend strength across multiple timeframes:
- **Short-term:** Clouds 1-2 (faster EMAs)
- **Medium-term:** Cloud 3 (intermediate EMAs)
- **Long-term:** Clouds 4-5 (slower EMAs)
## Trading Applications
### Trend Identification
- **Strong Uptrend:** Multiple clouds stacked bullishly with price above
- **Strong Downtrend:** Multiple clouds stacked bearishly with price below
- **Consolidation:** Mixed cloud colors indicating sideways movement
### Entry Signals
- **Bullish Entry:** Price breaking above bearish clouds turning bullish
- **Bearish Entry:** Price breaking below bullish clouds turning bearish
- **Confluence:** Multiple cloud confirmations strengthen signal reliability
### Support/Resistance Levels
- Cloud boundaries often act as dynamic support and resistance
- Thicker clouds (higher opacity) may provide stronger S/R levels
- Multiple cloud intersections create significant price levels
## Customization Guide
### Color Schemes
Create your own visual style by customizing:
1. **Bullish/Bearish colors** for each cloud pair
2. **Rising/Falling colors** for EMA lines
3. **Opacity levels** to layer clouds effectively
### Recommended Settings
- **Day Trading:** Focus on Clouds 1-2 with higher opacity
- **Swing Trading:** Use Clouds 1-3 with moderate opacity
- **Position Trading:** Emphasize Clouds 3-5 with lower opacity
## Technical Specifications
- **Version:** Pine Script v6
- **Type:** Overlay indicator
- **Calculations:** Real-time EMA computations
- **Performance:** Optimized for all timeframes
- **Alerts:** Configurable long/short alerts available
## Risk Disclaimer
This indicator is for educational and informational purposes only. Always combine with proper risk management and additional analysis before making trading decisions. Past performance does not guarantee future results.
---
*Enhanced and customized version of the original Ripster EMA Clouds by Ripster47. This modification adds comprehensive color customization and enhanced user control while preserving the core analytical framework.*
AltCoin & MemeCoin Index Correlation [Eddie_Bitcoin]🧠 Philosophy of the Strategy
The AltCoin & MemeCoin Index Correlation Strategy by Eddie_Bitcoin is a carefully engineered trend-following system built specifically for the highly volatile and sentiment-driven world of altcoins and memecoins.
This strategy recognizes that crypto markets—especially niche sectors like memecoins—are not only influenced by individual price action but also by the relative strength or weakness of their broader sector. Hence, it attempts to improve the reliability of trading signals by requiring alignment between a specific coin’s trend and its sector-wide index trend.
Rather than treating each crypto asset in isolation, this strategy dynamically incorporates real-time dominance metrics from custom indices (OTHERS.D and MEME.D) and combines them with local price action through dual exponential moving average (EMA) crossovers. Only when both the asset and its sector are moving in the same direction does it allow for trade entries—making it a confluence-based system rather than a single-signal strategy.
It supports risk-aware capital allocation, partial exits, configurable stop loss and take profit levels, and a scalable equity-compounding model.
✅ Why did I choose OTHERS.D and MEME.D as reference indices?
I selected OTHERS.D and MEME.D because they offer a sector-focused view of crypto market dynamics, especially relevant when trading altcoins and memecoins.
🔹 OTHERS.D tracks the market dominance of all cryptocurrencies outside the top 10 by market cap.
This excludes not only BTC and ETH, but also major stablecoins like USDT and USDC, making it a cleaner indicator of risk appetite across true altcoins.
🔹 This is particularly useful for detecting "Altcoin Season"—periods where capital rotates away from Bitcoin and flows into smaller-cap coins.
A rising OTHERS.D often signals the start of broader altcoin rallies.
🔹 MEME.D, on the other hand, captures the speculative behavior of memecoin segments, which are often driven by retail hype and social media activity.
It's perfect for timing momentum shifts in high-risk, high-reward tokens.
By using these indices, the strategy aligns entries with broader sector trends, filtering out noise and increasing the probability of catching true directional moves, especially in phases of capital rotation and altcoin risk-on behavior.
📐 How It Works — Core Logic and Execution Model
At its heart, this strategy employs dual EMA crossover detection—one pair for the asset being traded and one pair for the selected market index.
A trade is only executed when both EMA crossovers agree on the direction. For example:
Long Entry: Coin's fast EMA > slow EMA and Index's fast EMA > slow EMA
Short Entry: Coin's fast EMA < slow EMA and Index's fast EMA < slow EMA
You can disable the index filter and trade solely based on the asset’s trend just to make a comparison and see if improves a classic EMA crossover strategy.
Additionally, the strategy includes:
- Adaptive position sizing, based on fixed capital or current equity (compound mode)
- Take Profit and Stop Loss in percentage terms
- Smart partial exits when trend momentum fades
- Date filtering for precise backtesting over specific timeframes
- Real-time performance stats, equity tracking, and visual cues on chart
⚙️ Parameters & Customization
🔁 EMA Settings
Each EMA pair is customizable:
Coin Fast EMA: Default = 47
Coin Slow EMA: Default = 50
Index Fast EMA: Default = 47
Index Slow EMA: Default = 50
These control the sensitivity of the trend detection. A wider spread gives smoother, slower entries; a narrower spread makes it more responsive.
🧭 Index Reference
The correlation mechanism uses CryptoCap sector dominance indexes:
OTHERS.D: Dominance of all coins EXCLUDING Top 10 ones
MEME.D: Dominance of all Meme coins
These are dynamically calculated using:
OTHERS_D = OTHERS_cap / TOTAL_cap * 100
MEME_D = MEME_cap / TOTAL_cap * 100
You can select:
Reference Index: OTHERS.D or MEME.D
Or disable the index reference completely (Don't Use Index Reference)
💰 Position Sizing & Risk Management
Two capital allocation models are supported:
- Fixed % of initial capital (default)
- Compound profits, which scales positions as equity grows
Settings:
- Compound profits?: true/false
- % of equity: Between 1% and 200% (default = 10%)
This is critical for users who want to balance growth with risk.
🎯 Take Profit / Stop Loss
Customizable thresholds determine automatic exits:
- TakeProfit: Default = 99999 (disabled)
- StopLoss: Default = 5 (%)
These exits are percentage-based and operate off the entry price vs. current close.
📉 Trend Weakening Exit (Scale Out)
If the position is in profit but the trend weakens (e.g., EMA color signals trend loss), the strategy can partially close a configurable portion of the position:
- Scale Position on Weak Trend?: true/false
- Scaled Percentage: % to close (default = 65%)
This feature is useful for preserving profits without exiting completely.
📆 Date Filter
Useful for segmenting performance over specific timeframes (e.g., bull vs bear markets):
- Filter Date Range of Backtest: ON/OFF
- Start Date and End Date: Custom time range
OTHER PARAMETERS EXPLANATION (Strategy "Properties" Tab):
- Initial Capital is set to 100 USD
- Commission is set to 0.055% (The ones I have on Bybit)
- Slippage is set to 3 ticks
- Margin (short and long) are set to 0.001% to avoid "overspending" your initial capital allocation
📊 Visual Feedback and Debug Tools
📈 EMA Trend Visualization
The slow EMA line is dynamically color-coded to visually display the alignment between the asset trend and the index trend:
Lime: Coin and index both bullish
Teal: Only coin bullish
Maroon: Only index bullish
Red: Both bearish
This allows for immediate visual confirmation of current trend strength.
💬 Real-Time PnL Labels
When a trade closes, a label shows:
Previous trade return in % (first value is the effective PL)
Green background for profit, Red for losses.
📑 Summary Table Overlay
This table appears in a corner of the chart (user-defined) and shows live performance data including:
Trade direction (yellow long, purple short)
Emojis: 💚 for current profit, 😡 for current loss
Total number of trades
Win rate
Max drawdown
Duration in days
Current trade profit/loss (absolute and %)
Cumulative PnL (absolute and %)
APR (Annualized Percentage Return)
Each metric is color-coded:
Green for strong results
Yellow/orange for average
Red/maroon for poor performance
You can select where this appears:
Top Left
Top Right
Bottom Left
Bottom Right (default)
📚 Interpretation of Key Metrics
Equity Multiplier: How many times initial capital has grown (e.g., “1.75x”)
Net Profit: Total gains including open positions
Max Drawdown: Largest peak-to-valley drop in strategy equity
APR: Annualized return calculated based on equity growth and days elapsed
Win Rate: % of profitable trades
PnL %: Percentage profit on the most recent trade
🧠 Advanced Logic & Safety Features
🛑 “Don’t Re-Enter” Filter
If a trade is closed due to StopLoss without a confirmed reversal, the strategy avoids re-entering in that same direction until conditions improve. This prevents false reversals and repetitive losses in sideways markets.
🧷 Equity Protection
No new trades are initiated if equity falls below initial_capital / 30. This avoids overleveraging or continuing to trade when capital preservation is critical.
Keep in mind that past results in no way guarantee future performance.
Eddie Bitcoin
Pasrsifal.RegressionTrendStateSummary
The Parsifal.Regression.Trend.State Indicator analyzes the leading coefficients of linear and quadratic regressions of price (against time). It also considers their first- and second-order changes. These features are aggregated into a Trend-State background, shown as a gradient color. In addition, the indicator generates fast and slow signals that can be used as potential entry- or exit triggers.
This tool is designed for advanced trend-following strategies, leveraging information from multiple trendline features.
Background
Trendlines provide insight into the state of a trend or the “trendiness” of a price process. While moving averages or pivot-based lines can serve as envelopes and breakout levels, they are often too lagging for swing traders, who need tools that adapt more closely to price swings, ideally using trendlines, around which the price process swings continuously.
Regression lines address this by cutting directly through the data, making them a natural anchor for observing how price winds around a central trendline within a chosen lookback period.
Regression Trendlines
• Linear Regression:
o Minimizes distance to all closing values over the lookback period.
o The slope represents the short-term linear trend.
o The change of slope indicates trend acceleration or deceleration.
o Linear regression lags during phases of rapid market shifts.
• Quadratic Regression:
o Fits a second-degree polynomial to minimize deviation from closing prices.
o The convexity term (leading coefficient) reflects curvature:
Positive convexity → accelerating uptrend or fading downtrend.
Negative convexity → accelerating downtrend or fading uptrend.
o The change of convexity detects early shifts in momentum and often reacts faster than slope features.
Features Extracted
The indicator evaluates six features:
• Linear features: slope, first derivative of slope, second derivative of slope.
• Quadratic features: convexity term, first derivative of the convexity term, second derivative of the convexity term.
• Linear features: capture broad, background trend behavior.
• Quadratic features: detect deviations, accelerations, and smaller-scale dynamics.
Quadratic terms generally react first to market changes, while linear terms provide stability and context.
Dynamics of Market Moves as seen by linear and quadratic regressions
• At the start of a rapid move:
The change of convexity reacts first, capturing the shift in dynamics before other features. The convexity term then follows, while linear slope features lag further behind. Because convexity measures deviation from linearity, it reflects accelerating momentum more effectively than slope.
• At the end of a rapid move:
Again, the change of convexity responds first to fading momentum, signaling the transition from above-linear to below-linear dynamics. Even while a strong trend persists, the change of convexity may flip sign early, offering a warning of weakening strength. The convexity term itself adjusts more slowly but may still turn before the price process does. Linear features lag the most, typically only flipping after price has already reversed, thereby smoothing out the rapid, more sensitive reactions of quadratic terms.
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Parsifal Regression.Trend.State Method
1. Feature Mapping:
Each feature is mapped to a range between -1 and 1, preserving zero-crossings (critical for sign interpretation).
2. Aggregation:
A heuristic linear combination*) produces a background information value, visualized as a gradient color scale:
o Deep green → strong positive trend.
o Deep red → strong negative trend.
o Yellow → neutral or transitional states.
3. Signals:
o Fast signal (oscillator): ranges from -1 to 1, reflecting short-term trend state.
o Slow signal (smoothed): moving average of the fast signal.
o Their interactions (crossovers, zero-crossings) provide actionable trading triggers.
How to Use
The Trend-State background gradient provides intuitive visual feedback on the aggregated regression features (slope, convexity, and their changes). Because these features reflect not only current trend strength but also their acceleration or deceleration, the color transitions help anticipate evolving market states:
• Solid Green: All features near their highs. Indicates a strong, accelerating uptrend. May also reflect explosive or hyperbolic upside moves (including gaps).
• Fading Solid Green: A recently strong uptrend is losing momentum. Price may shift into a slower uptrend, consolidation, or even a reversal.
• Fading Green → Yellow: Often appears as a dirty yellow or a rapidly mixing pattern of green and red. Signals that the uptrend is weakening toward neutrality or beginning to turn negative.
• Yellow → Deepening Red: Two possible scenarios:
o Coming from a strong uptrend → suggests a sharp fade, though the trend may still technically be up.
o Coming from a weaker uptrend or sideways market → suggests the start of an accelerating downtrend.
• Solid Red: All features near their lows. Indicates a strong, accelerating downtrend. May also reflect crash-type conditions or downside gaps.
• Fading Solid Red: A recently strong downtrend is losing strength. Market may move into a slower decline, consolidation, or early reversal upward.
• Fading Red → Yellow : The downtrend is weakening toward neutral, with potential for a bullish shift.
• Yellow → Increasing Green: Two possible scenarios:
o Coming from a strong downtrend, it reflects a sharp fade of bearish momentum, though the market may still technically be trending down.
o Coming from a weaker downtrend or sideways movement, it suggests the start of an accelerating uptrend.
Note: Market evolution does not always follow this neat “color cycle.” It may jump between states, skip stages, or reverse abruptly depending on market conditions. This makes the background coloring particularly valuable as a contextual map of current and evolving price dynamics.
Signal Crossovers:
Although the fast signal is very similar (but not identical) to the background coloring, it provides a numerical representation indicating a bullish interpretation for rising values and bearish for falling.
o High-confidence entries:
Fast signal rising from < -0.7 and crossing above the slow signal → potential long entry.
Fast signal falling from > +0.7 and crossing below the slow signal → potential short entry.
o Low-confidence entries:
Crossovers near zero may still provide a valid trigger but may be noisy and should be confirmed with other signals.
o Zero-crossings:
Indicate broader state changes, useful for conservative positioning or option strategies. For confirmation of a Fast signal 0-crossing, wait for the Slow signal to cross as well.
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*) Note on Aggregation
While the indicator currently uses a heuristic linear combination of features, alternatives such as Principal Component Analysis (PCA) could provide a more formal aggregation. However, while in the absence of matrix algebra, the required eigenvalue decomposition can be approximated, its computational expense does not justify the marginal higher insight in this case. The current heuristic approach offers a practical balance of clarity, speed, and accuracy.
Painel Técnico (4H x 1D) — Clean UI + Alertas BrenoG📋 Main Functions
1️⃣ Analysis in two fixed timeframes
4 hours and 1 day analyzed in parallel.
Each column in the table displays the data for its respective timeframe.
2️⃣ Entry point based on oversold conditions
The “entry point” is not the current price, but rather the last candle that went into oversold territory (RSI ≤ configured threshold).
If there has been no recent oversold condition, the current price is used as a fallback.
All calculations (Buy Zone, Stops, TPs) are based on this point.
3️⃣ Buy Zone
Defined as:
java
Copiar
Editar
Low Zone = entry * (1 - width%)
High Zone = entry
Always visible in the table, but alerts can be set to trigger only if RSI is oversold at the moment of entry.
4️⃣ Automatic Stops
Moderate Stop and Conservative Stop, calculated as a % below the entry point.
Displayed in the table with black text on a gray background for emphasis.
Alerts trigger when price crosses below these levels.
5️⃣ Take Profits (TP1–TP4)
Calculated from the entry point:
By percentage (usePercentTP = true) or
By fixed prices (usePercentTP = false).
The table displays:
Target price
% gain over the entry point
They only appear when RSI > 50 and EMA50 > EMA200 (the “alignment” condition).
Alerts trigger only on breakouts upward.
6️⃣ Context Indicators
RSI → shows numeric value and green/red color.
MACD → indicates if the MACD line is above or below the signal line.
EMAs 50/200 → indicates “Golden Cross” or “Death Cross”.
Price vs EMA200 → dedicated row showing “Above” or “Below EMA 200” with green/red color.
7️⃣ Visual Panel
Semi–transparent dark gray background, thin borders.
Colored header:
Blue for 4H
Orange for 1D
Rows separated by data type for easy reading.
Configurable font size (tiny to large).
Table position configurable (top_left, top_right, etc.).
8️⃣ Integrated Alerts
Entry/Exit of Buy Zone
Touch of each TP
Touch of each Stop
RSI entering Oversold
All alerts are separated by timeframe with clear, fixed messages.
📌 Simple Summary:
It’s an intelligent panel that combines multi–timeframe technical analysis, automatic calculation of entries/stops/TPs based on oversold conditions, and ready–to–use alerts — all presented in a visual, compact, and fully configurable format.
Mig Trade Model - Kill Zones
Key features:
Liquidity Hunt Detection: Spots aggressive moves that "hunt" stops beyond recent swing highs/lows.
Consolidation Filter: Requires 1-3 small-range candles after a hunt before confirming with a strong candle.
Bias Application: Uses daily open/close to auto-detect bias or allows manual override.
Kill Zone Restriction: Limits signals to London (default: 7-10 AM UTC) and NY (default: 12-3 PM UTC) sessions for better relevance in active markets.
This strategy is inspired by smart money concepts (SMC) and ICT (Inner Circle Trader) methodologies, aiming to capture venom-like "stings" in price action where liquidity is grabbed before reversals.
How It Works
ATR Calculation: Uses a user-defined ATR length (default: 14) to measure volatility, which scales candle body and range thresholds.
Bias Determination:
Auto: Compares daily close to open (bullish if close > open).
Manual: User selects "Bullish" or "Bearish."
Strong Candles:
Bullish: Green candle with body > 2x ATR (configurable).
Bearish: Red candle with body > 2x ATR.
Small Range Candles:
Candles where high-low < 0.5x ATR (configurable).
Liquidity Hunt:
Bullish Hunt: Strong bearish candle making a new low below the past swing low (default: 10 bars).
Bearish Hunt: Strong bullish candle making a new high above the past swing high.
Signal Generation:
After a hunt, counts 1-3 small-range candles.
Confirms with a strong candle in the opposite direction (e.g., strong bullish after bearish hunt).
Resets if >3 small candles or an opposing strong candle appears.
Kill Zone Filter:
Checks if the current bar's time (in UTC) falls within London or NY Kill Zones.
Only allows final "Buy" (bullish entry) or "Sell" (bearish entry) if bias matches and in Kill Zone.
Plots:
Yellow circle (below): Bullish liquidity hunt.
Orange circle (above): Bearish liquidity hunt.
Blue diamond (below): Raw bullish signal.
Purple diamond (above): Raw bearish signal.
Green triangle up ("Buy"): Filtered bullish entry.
Red triangle down ("Sell"): Filtered bearish entry.
Inputs
Bias: "Auto" (default), "Bullish", or "Bearish" – Controls signal direction based on daily trend.
ATR Length: 14 (default) – Period for ATR calculation.
Swing Length for Liquidity Hunt: 10 (default) – Bars to look back for swing highs/lows.
Strong Candle Body Multiplier (x ATR): 2.0 (default) – Threshold for strong candle bodies.
Small Range Multiplier (x ATR): 0.5 (default) – Threshold for small-range candles.
London Kill Zone Start/End Hour (UTC): 7/10 (default) – Customize London session hours.
NY Kill Zone Start/End Hour (UTC): 12/15 (default) – Customize New York session hours.
Usage Tips
Timeframe: Best on lower timeframes (e.g., 5-15 min) for intraday trading, especially forex pairs like EURUSD or GBPUSD.
Timezone Adjustment: Inputs are in UTC. If your chart is in a different timezone (e.g., EST = UTC-5), adjust hours accordingly (e.g., London: 2-5 AM EST → 7-10 UTC).
Risk Management: Use with stop-loss (e.g., beyond the hunt low/high) and take-profit based on ATR multiples. Not financial advice—backtest thoroughly.
Customization: Tweak multipliers for different assets; higher for volatile cryptos, lower for stocks.
Limitations: Relies on historical data; may generate false signals in ranging markets. Combine with other indicators like volume or support/resistance.
This indicator is for educational purposes. Always use discretion and proper risk management in live trading. If you find it useful, feel free to share feedback or suggestions!
SMC_CommonLibrary "SMC_Common"
Common types and utilities for Smart Money Concepts indicators
get_future_time(bars_ahead)
Parameters:
bars_ahead (int)
get_time_at_offset(offset)
Parameters:
offset (int)
get_mid_time(time1, time2)
Parameters:
time1 (int)
time2 (int)
timeframe_to_string(tf)
Parameters:
tf (string)
is_psychological_level(price)
Parameters:
price (float)
detect_swing_high(src_high, lookback)
Parameters:
src_high (float)
lookback (int)
detect_swing_low(src_low, lookback)
Parameters:
src_low (float)
lookback (int)
detect_fvg(h, l, min_size)
Parameters:
h (float)
l (float)
min_size (float)
analyze_volume(vol, volume_ma)
Parameters:
vol (float)
volume_ma (float)
create_label(x, y, label_text, bg_color, label_size, use_time)
Parameters:
x (int)
y (float)
label_text (string)
bg_color (color)
label_size (string)
use_time (bool)
SwingPoint
Fields:
price (series float)
bar_index (series int)
bar_time (series int)
swing_type (series string)
strength (series int)
is_major (series bool)
timeframe (series string)
LiquidityLevel
Fields:
price (series float)
bar_index (series int)
bar_time (series int)
liq_type (series string)
touch_count (series int)
is_swept (series bool)
quality_score (series float)
level_type (series string)
OrderBlock
Fields:
start_bar (series int)
end_bar (series int)
start_time (series int)
end_time (series int)
top (series float)
bottom (series float)
ob_type (series string)
has_liquidity_sweep (series bool)
has_fvg (series bool)
is_mitigated (series bool)
is_breaker (series bool)
timeframe (series string)
mitigation_level (series float)
StructureBreak
Fields:
level (series float)
break_bar (series int)
break_time (series int)
break_type (series string)
direction (series string)
is_confirmed (series bool)
source_swing_bar (series int)
source_time (series int)
SignalData
Fields:
signal_type (series string)
entry_price (series float)
stop_loss (series float)
take_profit (series float)
risk_reward_ratio (series float)
confluence_count (series int)
confidence_score (series float)
strength (series string)
BTC 1m Chop Top/Bottom Reversal (Stable Entries)Strategy Description: BTC 5m Chop Top/Bottom Reversal (Stable Entries)
This strategy is engineered to capture precise reversal points during Bitcoin’s choppy or sideways price action on the 5-minute timeframe. It identifies short-term tops and bottoms using a confluence of volatility bands, momentum indicators, and price structure, optimized for high-probability scalping and intraday reversals.
Core Logic:
Volatility Filter: Uses an EMA with ATR bands to define overextended price zones.
Momentum Divergence: Confirms reversals using RSI and MACD histogram shifts.
Price Action Filter: Requires candle confirmation in the direction of the trade.
Locked Signal Logic: Prevents repaints and disappearing trades by confirming signals only once per bar.
Trade Parameters:
Short Entry: Above upper band + overbought RSI + weakening MACD + bearish candle
Long Entry: Below lower band + oversold RSI + strengthening MACD + bullish candle
Take Profit: ±0.75%
Stop Loss: ±0.4%
This setup is tuned for traders using tight risk control and leverage, where execution precision and minimal drawdown tolerance are critical.
Smart Order Blocks [Pro Version]Here’s a **clear, detailed "How It Works" explanation** for this indicator:
---
## ✅ **Smart Order Blocks \ – How It Works**
### **Purpose**
This indicator detects **Order Blocks (OBs)** based on **pivot highs and lows**, and automatically marks **Bullish** and **Bearish OB zones** on the chart with optional extensions and alerts. It is designed to help traders identify **institutional price levels** where liquidity is often engineered for future price moves.
---
### **Customization Options**
✔ **Source** → Choose between Wicks or Bodies for OB calculation.
✔ **Pivot Settings** → Adjust sensitivity for detecting pivots.
✔ **Extend OBs** → Keep zones visible until tapped, or fix a specific width.
✔ **Show Labels** → Displays OB type and strength on chart.
✔ **Colors** → Configure Bullish, Bearish, and Invalid OB colors.
---
### **Practical Usage**
* **Entry Strategy**:
* Wait for price to **revisit a Bullish OB** in an uptrend → Long entry.
* Wait for price to **revisit a Bearish OB** in a downtrend → Short entry.
* Combine with:
* **Market Structure (HH/HL or LH/LL)**.
* **Confirmation signals** (e.g., candlestick pattern, break of structure).
* **Risk Management** → Stop loss outside OB zone.
---
### ✅ **Summary in One Sentence**
The indicator automatically identifies **institutional OB zones**, shows their strength, extends them until mitigated, and alerts you when price interacts with these key liquidity levels, helping you trade like Smart Money.
---
Gann Single Square Swing Trading System with Gann AnglesGann Single Square Swing Trading System
This script automatically detects "squares" - geometric patterns where price movement equals time movement. When price moves the same distance as the number of bars (time), it creates powerful support/resistance levels based on Gann theory.
Key Visual Elements
• Box: The detected square pattern
• Dark Blue Line (50%): Most important trading level
• Green Lines: Profit target levels (125%, 150%)
• Red Lines: Stop loss levels (-25%, -50%)
• Colored Angle Lines: Gann angles for trend direction
• Quality Score: Blue label showing setup strength (aim for 70%+)
Simple Trading Rules
LONG Trades (Green 🟢 Square)
1. Entry: Buy when price touches the dark blue 50% line from above
2. Stop Loss: Place below the red -25% line
3. Take Profit: Exit at green 125% line (first target) or 150% line (second target)
SHORT Trades (Red 🔴 Square)
1. Entry: Sell when price touches the dark blue 50% line from below
2. Stop Loss: Place above the red -25% line
3. Take Profit: Exit at green 125% line (first target) or 150% line (second target)
Entry Checklist
✅ Square quality score > 70%
✅ Price touches 50% level (dark blue line)
✅ Volume above average (if volume filter enabled)
✅ Clear square formation visible
Alerts
The script generates automatic alerts when price reaches the 50% trading level. Enable alerts in TradingView to get notified of setups.
Bottom Line: Wait for the alert → Check quality score → Enter at 50% level → Set stop at red line → Take profit at green line.
US Index First 30m Candle Strategy (10m Chart)Strategy Description for Publishing
Title: US Index First 30-Minute Candle Strategy (10m Chart)
Overview:
This Pine Script implements a trading strategy designed to capitalize on price movements within the first 30 minutes of the U.S. stock market opening. It is specifically tailored for use on a 15-minute chart and is optimized for trading U.S. indices during regular market hours.
Features:
Session Time Configuration: The strategy operates within the U.S. market hours, specifically from 9:30 AM to 4:00 PM (Eastern Time).
First 30-Minute Candle Aggregation: The script identifies the high and low of the first 30-minute candle, which is considered a critical time frame for market momentum.
Single Trade Per Day: To minimize risk, the strategy is designed to execute only one trade per day based on the established range of the first 30 minutes.
Dynamic Trade Conditions: Buy and sell signals are generated when the price breaks above the high or below the low of the first 30-minute candle, with defined stop-loss and take-profit levels based on a customizable risk-reward ratio.
How It Works:
Initialization:
At the start of each trading day, the script resets the high and low values for the first 30 minutes.
Range Locking: After the first 30 minutes, the high and low values are locked, allowing for trade entries based on these levels.
Trade Execution:
Long Entry: Triggered when the price moves above the locked high.
Short Entry: Triggered when the price drops below the locked low.
Risk Management: Each trade comes with a stop-loss and take-profit mechanism to manage potential losses and secure profits.
Visuals:
The script also plots the locked high and low levels on the chart, providing a visual reference for traders.
Conclusion:
This strategy leverages the volatility often seen in the first 30 minutes of trading, aiming to capture significant price movements while maintaining a disciplined trading approach. It is suitable for traders looking to implement a systematic strategy based on early market behavior.
Usage:
To use this strategy, simply add the script to your TradingView chart, set your desired parameters, and monitor for trade signals during the specified market hours. Adjust the risk-reward ratio as needed to align with your trading style.
Mickey's EMAMickey’s EMA is a lightweight, overlay indicator that combines two Exponential Moving Averages (EMAs) with automatic entry, stop-loss and target visual signals—plus dynamic JSON alerts for seamless webhook integration. It’s designed for both day-traders and swing-traders who want clear, on-chart cues and fully-customizable risk parameters.
🔍 Overview
Dual EMAs (fast & slow) to capture trend changes.
Automated “BUY” / “SELL” markers at every EMA crossover.
Customizable Stop-Loss % and Target % levels, plotted as ❌ and 🎯 bubbles.
“SL Hit (Custom)” if the opposite EMA crossover occurs before price touches your stop level.
JSON-formatted alerts containing ticker, instrument type, timeframe, trend (“CE” for bullish, “PE” for bearish), and price—ready for webhooks.
⚙️ Inputs
| Setting | Default | Description |
| ------------------------ | ------- | ----------------------------------------------- |
| **Fast EMA Length** | 20 | Period for the faster EMA. |
| **Slow EMA Length** | 200 | Period for the slower EMA. |
| **Price Source** | Close | Data series to calculate EMAs on. |
| **Custom Stop Loss %** | 0.1% | Stop-loss level as a percentage of entry price. |
| **Target %** | 0.5% | Profit-target level as a percentage of entry. |
| **Show Entry/SL/Target** | ON | Toggle all entry, SL and target visuals. |
📊 What It Plots
Fast EMA (blue) & Slow EMA (white) overlayed on price.
BUY 🟢 label below bar when Fast EMA crosses above Slow EMA.
SELL 🔴 label above bar when Fast EMA crosses below Slow EMA.
❌ (Custom) bubble at entry price if an opposite EMA crossover occurs before price hits your custom stop-loss.
❌ bubble at the stop-loss price when price actually breaches the stop level.
🎯 bubble at target price when price first reaches your profit-target level.
🔔 Alerts & Webhooks
On-screen alert conditions “Mickey’s EMA → BUY” and “Mickey’s EMA → SELL” appear in the Create-Alert dialog.
Dynamic JSON payload sent via alert() when a crossover fires, e.g.:
{
"script": "AAPL",
"scriptType": "equity",
"instrumentType": "NASDAQ",
"timeframe": "5",
"trend": "CE",
"price": 174.25
}
Use these alerts to integrate with bots, chat systems, manual, or any webhook-driven workflow.
🚀 Why Use Mickey’s EMA?
Clarity & Precision: All signals appear exactly at the EMA or price-level of interest.
Custom Risk Management: Define your own stop-loss and target percentages.
Seamless Automation: Dynamic JSON alerts mean zero manual setup for webhooks.
Versatile: Equally effective on intraday charts or daily/weekly timeframes.
Add Mickey’s EMA to your TradingView chart today and get instant, aesthetically-pleasing guidance on trend entries, risk exits, and profit targets—all in one elegant overlay.
Chaikin Oscillator Enhanced📊 What Is the Chaikin Oscillator?
The Chaikin Oscillator is a momentum indicator that helps traders understand the strength of buying and selling pressure in the market, based on volume and price movement.
It is calculated as the difference between two moving averages (short-term and long-term) of the Accumulation/Distribution Line (A/D Line). This line combines price and volume to show whether money is flowing into or out of an asset.
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🧠 Simple Concept
• When big traders are buying, they usually do so with volume support—the Chaikin Oscillator picks this up.
• When volume is rising but price is falling, or vice versa, it shows hidden strength or weakness.
So, this indicator helps you see what the smart money is doing, even if the price isn’t moving much.
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🛠️ How It Works
• Oscillator Value Above Zero → More buying pressure (bullish).
• Oscillator Value Below Zero → More selling pressure (bearish).
• Crossing above zero → A potential buy signal.
• Crossing below zero → A potential sell signal.
The histogram (vertical bars) in the indicator changes color:
• Green bars = Positive momentum.
• Red bars = Negative momentum.
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🎯 How Traders Use It for Entry and Exit
✅ For Entries:
• Buy Entry: When the oscillator crosses above the zero line and the bars turn green, it means buyers are stepping in with volume.
• For better confirmation, combine it with price breaking above a resistance level.
❌ For Exits or Shorts:
• Sell Exit or Short Entry: When the oscillator crosses below the zero line and bars turn red, it suggests selling pressure is growing.
• If the price is also below support, it’s a stronger signal.
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🔍 Example Use Case:
1. You’re watching a stock or crypto that's been going sideways.
2. Suddenly, the Chaikin Oscillator crosses above zero, and green bars appear.
3. That’s your early clue that big buyers might be entering.
4. If price confirms this with a breakout, you can enter a long position.
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🌐 Where Is It Useful?
The Chaikin Oscillator is great for:
• Stocks (especially volume-heavy large caps)
• ETFs
• Cryptocurrency (on exchanges that provide volume data)
• Forex – less reliable unless volume is proxy-based
⚠️ Important: It won’t work well on instruments where volume data is missing or unreliable (like some CFDs or synthetic assets).
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🧭 Pro Tips for Using It:
• Combine it with support/resistance, moving averages, or candlestick patterns.
• Avoid trading only based on this indicator—use it as confirmation.
• Use the alerts (added in the script) so you don’t miss key movements.
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HMA Crossover + ATR + Curvature (Long & Short)📏 Hull Moving Averages (Trend Filters)
- fastHMA = ta.hma(close, fastLength)
- slowHMA = ta.hma(close, slowLength)
These two HMAs act as dynamic trend indicators:
- A bullish crossover of fast over slow HMA signals a potential long setup.
- A bearish crossunder triggers short interest.
⚡️ Curvature (Acceleration Filter)
- curv = ta.change(ta.change(fastHMA))
This calculates the second-order change (akin to the second derivative) of the fast HMA — effectively the acceleration of the trend. It serves as a filter:
- For long entries: curv > curvThresh (positive acceleration)
- For short entries: curv < -curvThresh (negative acceleration)
It helps eliminate weak or stagnating moves by requiring momentum behind the crossover.
📈 Volatility-Based Risk Management (ATR)
- atr = ta.atr(atrLength)
- stopLoss = atr * atrMult
- trailStop = atr * trailMult
These define your:
- Initial stop loss: scaled to recent volatility using ATR and atrMult.
- Trailing stop: also ATR-scaled, to lock in gains dynamically as price moves favorably.
💰 Position Sizing via Risk Percent
- capital = strategy.equity
- riskCapital = capital * (riskPercent / 100)
- qty = riskCapital / stopLoss
This dynamically calculates the position size (qty) such that if the stop loss is hit, the loss does not exceed the predefined percentage of account equity. It’s a volatility-adjusted position sizing method, keeping your risk consistent regardless of market conditions.
📌 Execution Logic
- Long Entry: on bullish HMA crossover with rising curvature.
- Short Entry: on bearish crossover with falling curvature.
- Exits: use ATR-based trailing stops.
- Position is closed when trend conditions reverse (e.g., bearish crossover exits the long).
This framework gives you:
- Trend-following logic (via HMAs)
- Momentum confirmation (via curvature)
- Volatility-aware execution and exits (via ATR)
- Risk-controlled dynamic sizing
Want to get surgical and test what happens if we use curvature on the difference between HMAs instead? That might give some cool insights into trend strength transitions.
