6 Dynamic EMAs by Koenigsegg🚀 6 Dynamic EMAs by Koenigsegg
Take control of your chart with ultimate flexibility. This tool gives you 6 customizable EMAs across any timeframe, helping you read the market like a pro — whether you're scalping seconds or swinging days. Built for precision, designed for dominance.
The combinations? Endless. Mix and match any EMA lengths and timeframes for tailored confluence — exactly how elite traders operate.
🔑 Key Features
✅ 6 Fully Customizable EMAs
⏳ Multi-Timeframe Support (from seconds to months)
🎨 Custom Colors & Thickness for each EMA
🚨 Built-in Cross Alerts for instant trade signals
🧠 Clean, efficient logic using request.security()
🔁 Dynamically toggle EMAs on/off
⚙️ Lightweight for smooth chart performance
🧩 Endless combo potential — confluence on your terms
📈 What Is an EMA?
The EMA is a type of moving average that adjusts more quickly to recent price changes than a Simple Moving Average (SMA). It does this by giving exponentially more weight to the most recent candles.
⚙️ How Does It Function?
Smoothing Price Data:
It takes the average of closing prices over a chosen period (like 20 or 50 candles), but gives more influence to the latest prices.
Reacts Quickly to Price Shifts:
Since recent data is weighted more heavily, the EMA adjusts faster to sudden price changes — helping you spot trend reversals or momentum shifts earlier.
Dynamic Support & Resistance:
Traders often use EMAs as moving support/resistance levels. Price often "respects" EMAs in trending markets — bouncing off them during pullbacks.
Trend Confirmation:
- If price is above the EMA, the market is likely in an uptrend.
- If price is below the EMA, the market is likely in a downtrend.
- Multiple EMAs (like 12/21 or 50/200) crossing each other are used for entry/exit signals.
💡 Example:
If you use a 21 EMA on a chart, it shows you the average price of the last 21 candles, but the most recent ones weigh heavier. This makes the EMA more responsive than an SMA, and better for short-term or active trading.
📊 Why EMAs Matter — and How Multi-Timeframe EMAs Give You the Edge
Exponential Moving Averages (EMAs) are essential tools for identifying trend direction, momentum shifts, and dynamic support/resistance. Because they weight recent price data more heavily, EMAs adapt quickly to changing market conditions, giving traders early insight into reversals or continuations.
Where this script shines is in its multi-timeframe (MTF) capability. For example, plotting a daily EMA on a 4H chart gives you high-level directional guidance while still allowing precision entries. This enables confluence between LTF (low timeframe) signals and HTF (high timeframe) momentum — a crucial edge used by institutional-level traders.
You can configure the tool to run classic combos like the 12/21 crossover on your current chart, while layering in a 50 or 200 EMA from a higher timeframe for macro confirmation. The 6th EMA, colored light blue by default, is perfect for adding one final level of structure insight — often used as a long-term anchor or trend bias marker.
Whether you're riding the wave or catching the reversal, these EMAs serve as your adaptable compass in every environment.
🎯 Purpose
This indicator was built to give traders a clear, responsive, and multi-timeframe edge using dynamic Exponential Moving Averages. Whether you're trend-following, identifying momentum shifts, or building a confluence system — these 6 EMAs are here to align with your strategy and style.
💡 Pro Tip
Instead of cluttering your chart with multiple EMA indicators, this script consolidates all into one sleek tool. You can toggle off bands you don't currently need, like running only the 12/21 EMAs on your active chart timeframe, while adding the 12/21 EMAs from a higher timeframe to guide trade decisions.
With this setup, you're not just reacting — you're orchestrating your trades with intention.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice. Always do your own research and trade responsibly. Past performance does not guarantee future results.
Cari dalam skrip untuk "TAKE"
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Dskyz (DAFE) Aurora Divergence – Quant Master Dskyz (DAFE) Aurora Divergence – Quant Master
Introducing the Dskyz (DAFE) Aurora Divergence – Quant Master , a strategy that’s your secret weapon for mastering futures markets like MNQ, NQ, MES, and ES. Born from the legendary Aurora Divergence indicator, this fully automated system transforms raw divergence signals into a quant-grade trading machine, blending precision, risk management, and cyberpunk DAFE visuals that make your charts glow like a neon skyline. Crafted with care and driven by community passion, this strategy stands out in a sea of generic scripts, offering traders a unique edge to outsmart institutional traps and navigate volatile markets.
The Aurora Divergence indicator was a cult favorite for spotting price-OBV divergences with its aqua and fuchsia orbs, but traders craved a system to act on those signals with discipline and automation. This strategy delivers, layering advanced filters (z-score, ATR, multi-timeframe, session), dynamic risk controls (kill switches, adaptive stops/TPs), and a real-time dashboard to turn insights into profits. Whether you’re a newbie dipping into futures or a pro hunting reversals, this strat’s got your back with a beginner guide, alerts, and visuals that make trading feel like a sci-fi mission. Let’s dive into every detail and see why this original DAFE creation is a must-have.
Why Traders Need This Strategy
Futures markets are a battlefield—fast-paced, volatile, and riddled with institutional games that can wipe out undisciplined traders. From the April 28, 2025 NQ 1k-point drop to sneaky ES slippage, the stakes are high. Meanwhile, platforms are flooded with unoriginal, low-effort scripts that promise the moon but deliver noise. The Aurora Divergence – Quant Master rises above, offering:
Unmatched Originality: A bespoke system built from the ground up, with custom divergence logic, DAFE visuals, and quant filters that set it apart from copycat clutter.
Automation with Precision: Executes trades on divergence signals, eliminating emotional slip-ups and ensuring consistency, even in chaotic sessions.
Quant-Grade Filters: Z-score, ATR, multi-timeframe, and session checks filter out noise, targeting high-probability reversals.
Robust Risk Management: Daily loss and rolling drawdown kill switches, plus ATR-based stops/TPs, protect your capital like a fortress.
Stunning DAFE Visuals: Aqua/fuchsia orbs, aurora bands, and a glowing dashboard make signals intuitive and charts a work of art.
Community-Driven: Evolved from trader feedback, this strat’s a labor of love, not a recycled knockoff.
Traders need this because it’s a complete, original system that blends accessibility, sophistication, and style. It’s your edge to trade smarter, not harder, in a market full of traps and imitators.
1. Divergence Detection (Core Signal Logic)
The strategy’s core is its ability to detect bullish and bearish divergences between price and On-Balance Volume (OBV), pinpointing reversals with surgical accuracy.
How It Works:
Price Slope: Uses linear regression over a lookback (default: 9 bars) to measure price momentum (priceSlope).
OBV Slope: OBV tracks volume flow (+volume if price rises, -volume if falls), with its slope calculated similarly (obvSlope).
Bullish Divergence: Price slope negative (falling), OBV slope positive (rising), and price above 50-bar SMA (trend_ma).
Bearish Divergence: Price slope positive (rising), OBV slope negative (falling), and price below 50-bar SMA.
Smoothing: Requires two consecutive divergence bars (bullDiv2, bearDiv2) to confirm signals, reducing false positives.
Strength: Divergence intensity (divStrength = |priceSlope * obvSlope| * sensitivity) is normalized (0–1, divStrengthNorm) for visuals.
Why It’s Brilliant:
- Divergences catch hidden momentum shifts, often exploited by institutions, giving you an edge on reversals.
- The 50-bar SMA filter aligns signals with the broader trend, avoiding choppy markets.
- Adjustable lookback (min: 3) and sensitivity (default: 1.0) let you tune for different instruments or timeframes.
2. Filters for Precision
Four advanced filters ensure signals are high-probability and market-aligned, cutting through the noise of volatile futures.
Z-Score Filter:
Logic: Calculates z-score ((close - SMA) / stdev) over a lookback (default: 50 bars). Blocks entries if |z-score| > threshold (default: 1.5) unless disabled (useZFilter = false).
Impact: Avoids trades during extreme price moves (e.g., blow-off tops), keeping you in statistically safe zones.
ATR Percentile Volatility Filter:
Logic: Tracks 14-bar ATR in a 100-bar window (default). Requires current ATR > 80th percentile (percATR) to trade (tradeOk).
Impact: Ensures sufficient volatility for meaningful moves, filtering out low-volume chop.
Multi-Timeframe (HTF) Trend Filter:
Logic: Uses a 50-bar SMA on a higher timeframe (default: 60min). Longs require price > HTF MA (bullTrendOK), shorts < HTF MA (bearTrendOK).
Impact: Aligns trades with the bigger trend, reducing counter-trend losses.
US Session Filter:
Logic: Restricts trading to 9:30am–4:00pm ET (default: enabled, useSession = true) using America/New_York timezone.
Impact: Focuses on high-liquidity hours, avoiding overnight spreads and erratic moves.
Evolution:
- These filters create a robust signal pipeline, ensuring trades are timed for optimal conditions.
- Customizable inputs (e.g., zThreshold, atrPercentile) let traders adapt to their style without compromising quality.
3. Risk Management
The strategy’s risk controls are a masterclass in balancing aggression and safety, protecting capital in volatile markets.
Daily Loss Kill Switch:
Logic: Tracks daily loss (dayStartEquity - strategy.equity). Halts trading if loss ≥ $300 (default) and enabled (killSwitch = true, killSwitchActive).
Impact: Caps daily downside, crucial during events like April 27, 2025 ES slippage.
Rolling Drawdown Kill Switch:
Logic: Monitors drawdown (rollingPeak - strategy.equity) over 100 bars (default). Stops trading if > $1000 (rollingKill).
Impact: Prevents prolonged losing streaks, preserving capital for better setups.
Dynamic Stop-Loss and Take-Profit:
Logic: Stops = entry ± ATR * multiplier (default: 1.0x, stopDist). TPs = entry ± ATR * 1.5x (profitDist). Longs: stop below, TP above; shorts: vice versa.
Impact: Adapts to volatility, keeping stops tight but realistic, with TPs targeting 1.5:1 reward/risk.
Max Bars in Trade:
Logic: Closes trades after 8 bars (default) if not already exited.
Impact: Frees capital from stagnant trades, maintaining efficiency.
Kill Switch Buffer Dashboard:
Logic: Shows smallest buffer ($300 - daily loss or $1000 - rolling DD). Displays 0 (red) if kill switch active, else buffer (green).
Impact: Real-time risk visibility, letting traders adjust dynamically.
Why It’s Brilliant:
- Kill switches and ATR-based exits create a safety net, rare in generic scripts.
- Customizable risk inputs (maxDailyLoss, dynamicStopMult) suit different account sizes.
- Buffer metric empowers disciplined trading, a DAFE signature.
4. Trade Entry and Exit Logic
The entry/exit rules are precise, filtered, and adaptive, ensuring trades are deliberate and profitable.
Entry Conditions:
Long Entry: bullDiv2, cooldown passed (canSignal), ATR filter passed (tradeOk), in US session (inSession), no kill switches (not killSwitchActive, not rollingKill), z-score OK (zOk), HTF trend bullish (bullTrendOK), no existing long (lastDirection != 1, position_size <= 0). Closes shorts first.
Short Entry: Same, but for bearDiv2, bearTrendOK, no long (lastDirection != -1, position_size >= 0). Closes longs first.
Adaptive Cooldown: Default 2 bars (cooldownBars). Doubles (up to 10) after a losing trade, resets after wins (dynamicCooldown).
Exit Conditions:
Stop-Loss/Take-Profit: Set per trade (ATR-based). Exits on stop/TP hits.
Other Exits: Closes if maxBarsInTrade reached, ATR filter fails, or kill switch activates.
Position Management: Ensures no conflicting positions, closing opposites before new entries.
Built To Be Reliable and Consistent:
- Multi-filtered entries minimize false signals, a stark contrast to basic scripts.
- Adaptive cooldown prevents overtrading, especially after losses.
- Clean position handling ensures smooth execution, even in fast markets.
5. DAFE Visuals
The visuals are a DAFE hallmark, blending function with clean flair to make signals intuitive and charts stunning.
Aurora Bands:
Display: Bands around price during divergences (bullish: below low, bearish: above high), sized by ATR * bandwidth (default: 0.5).
Colors: Aqua (bullish), fuchsia (bearish), with transparency tied to divStrengthNorm.
Purpose: Highlights divergence zones with a glowing, futuristic vibe.
Divergence Orbs:
Display: Large/small circles (aqua below for bullish, fuchsia above for bearish) when bullDiv2/bearDiv2 and canSignal. Labels show strength (0–1).
Purpose: Pinpoints entries with eye-catching clarity.
Gradient Background:
Display: Green (bullish), red (bearish), or gray (neutral), 90–95% transparent.
Purpose: Sets the market mood without clutter.
Strategy Plots:
- Stop/TP Lines: Red (stops), green (TPs) for active trades.
- HTF MA: Yellow line for trend context.
- Z-Score: Blue step-line (if enabled).
- Kill Switch Warning: Red background flash when active.
What Makes This Next-Level?:
- Visuals make complex signals (divergences, filters) instantly clear, even for beginners.
- DAFE’s unique aesthetic (orbs, bands) sets it apart from generic scripts, reinforcing originality.
- Functional plots (stops, TPs) enhance trade management.
6. Metrics Dashboard
The top-right dashboard (2x8 table) is your command center, delivering real-time insights.
Metrics:
Daily Loss ($): Current loss vs. day’s start, red if > $300.
Rolling DD ($): Drawdown vs. 100-bar peak, red if > $1000.
ATR Threshold: Current percATR, green if ATR exceeds, red if not.
Z-Score: Current value, green if within threshold, red if not.
Signal: “Bullish Div” (aqua), “Bearish Div” (fuchsia), or “None” (gray).
Action: “Consider Buying”/“Consider Selling” (signal color) or “Wait” (gray).
Kill Switch Buffer ($): Smallest buffer to kill switch, green if > 0, red if 0.
Why This Is Important?:
- Consolidates critical data, making decisions effortless.
- Color-coded metrics guide beginners (e.g., green action = go).
- Buffer metric adds transparency, rare in off-the-shelf scripts.
7. Beginner Guide
Beginner Guide: Middle-right table (shown once on chart load), explains aqua orbs (bullish, buy) and fuchsia orbs (bearish, sell).
Key Features:
Futures-Optimized: Tailored for MNQ, NQ, MES, ES with point-value adjustments.
Highly Customizable: Inputs for lookback, sensitivity, filters, and risk settings.
Real-Time Insights: Dashboard and visuals update every bar.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
User-Friendly: Guide, visuals, and dashboard make it accessible yet powerful.
Original Design: DAFE’s unique logic and visuals stand out from generic scripts.
How to Use
Add to Chart: Load on a 5min MNQ/ES chart in TradingView.
Configure Inputs: Adjust instrument, filters, or risk (defaults optimized for MNQ).
Monitor Dashboard: Watch signals, actions, and risk metrics (top-right).
Backtest: Run in strategy tester to evaluate performance.
Live Trade: Connect to a broker (e.g., Tradovate) for automation. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Use bar replay (e.g., April 28, 2025 NQ drop) to test volatility handling.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Backtest results may not reflect live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Aurora Divergence – Quant Master isn’t just a strategy—it’s a movement. Crafted with originality and driven by community passion, it rises above the flood of generic scripts to deliver a system that’s as powerful as it is beautiful. With its quant-grade logic, DAFE visuals, and robust risk controls, it empowers traders to tackle futures with confidence and style. Join the DAFE crew, light up your charts, and let’s outsmart the markets together!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
Prop Firm Business SimulatorThe prop firm business simulator is exactly what it sounds like. It's a plug and play tool to test out any tradingview strategy and simulate hypothetical performance on CFD Prop Firms.
Now what is a modern day CFD Prop Firm?
These companies sell simulated trading challenges for a challenge fee. If you complete the challenge you get access to simulated capital and you get a portion of the profits you make on those accounts payed out.
I've included some popular firms in the code as presets so it's easy to simulate them. Take into account that this info will likely be out of date soon as these prices and challenge conditions change.
Also, this tool will never be able to 100% simulate prop firm conditions and all their rules. All I aim to do with this tool is provide estimations.
Now why is this tool helpful?
Most traders on here want to turn their passion into their full-time career, prop firms have lately been the buzz in the trading community and market themselves as a faster way to reach that goal.
While this all sounds great on paper, it is sometimes hard to estimate how much money you will have to burn on challenge fees and set realistic monthly payout expectations for yourself and your trading. This is where this tool comes in.
I've specifically developed this for traders that want to treat prop firms as a business. And as a business you want to know your monthly costs and income depending on the trading strategy and prop firm challenge you are using.
How to use this tool
It's quite simple you remove the top part of the script and replace it with your own strategy. Make sure it's written in same version of pinescript before you do that.
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
//--$$$$$--Strategy-- --$$$$$$--// ******************************************************************************************************************************
//--$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$--//--------------------------------------------------------------------------------------------------------------------------$$$$$$
length = input.int(20, minval=1, group="Keltner Channel Breakout")
mult = input(2.0, "Multiplier", group="Keltner Channel Breakout")
src = input(close, title="Source", group="Keltner Channel Breakout")
exp = input(true, "Use Exponential MA", display = display.data_window, group="Keltner Channel Breakout")
BandsStyle = input.string("Average True Range", options = , title="Bands Style", display = display.data_window, group="Keltner Channel Breakout")
atrlength = input(10, "ATR Length", display = display.data_window, group="Keltner Channel Breakout")
esma(source, length)=>
s = ta.sma(source, length)
e = ta.ema(source, length)
exp ? e : s
ma = esma(src, length)
rangema = BandsStyle == "True Range" ? ta.tr(true) : BandsStyle == "Average True Range" ? ta.atr(atrlength) : ta.rma(high - low, length)
upper = ma + rangema * mult
lower = ma - rangema * mult
//--Graphical Display--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
u = plot(upper, color=#2962FF, title="Upper", force_overlay=true)
plot(ma, color=#2962FF, title="Basis", force_overlay=true)
l = plot(lower, color=#2962FF, title="Lower", force_overlay=true)
fill(u, l, color=color.rgb(33, 150, 243, 95), title="Background")
//--Risk Management--// *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-$$$$$$
riskPerTradePerc = input.float(1, title="Risk per trade (%)", group="Keltner Channel Breakout")
le = high>upper ? false : true
se = lowlower
strategy.entry('PivRevLE', strategy.long, comment = 'PivRevLE', stop = upper, qty=riskToLots)
if se and upper>lower
strategy.entry('PivRevSE', strategy.short, comment = 'PivRevSE', stop = lower, qty=riskToLots)
The tool will then use the strategy equity of your own strategy and use this to simulat prop firms. Since these CFD prop firms work with different phases and payouts the indicator will simulate the gains until target or max drawdown / daily drawdown limit gets reached. If it reaches target it will go to the next phase and keep on doing that until it fails a challenge.
If in one of the phases there is a reward for completing, like a payout, refund, extra it will add this to the gains.
If you fail the challenge by reaching max drawdown or daily drawdown limit it will substract the challenge fee from the gains.
These gains are then visualised in the calendar so you can get an idea of yearly / monthly gains of the backtest. Remember, it is just a backtest so no guarantees of future income.
The bottom pane (non-overlay) is visualising the performance of the backtest during the phases. This way u can check if it is realistic. For instance if it only takes 1 bar on chart to reach target you are probably risking more than the firm wants you to risk. Also, it becomes much less clear if daily drawdown got hit in those high risk strategies, the results will be less accurate.
The daily drawdown limit get's reset every time there is a new dayofweek on chart.
If you set your prop firm preset setting to "'custom" the settings below that are applied as your prop firm settings. Otherwise it will use one of the template by default it's FTMO 100K.
The strategy I'm using as an example in this script is a simple Keltner Channel breakout strategy. I'm using a 0.05% commission per trade as that is what I found most common on crypto exchanges and it's close to the commissions+spread you get on a cfd prop firm. I'm targeting a 1% risk per trade in the backtest to try and stay within prop firm boundaries of max 1% risk per trade.
Lastly, the original yearly and monthly performance table was developed by Quantnomad and I've build ontop of that code. Here's a link to the original publication:
That's everything for now, hope this indicator helps people visualise the potential of prop firms better or to understand that they are not a good fit for their current financial situation.
Collatz Conjecture - DolphinTradeBot1️⃣ Overview
Every positive number follows its own unique path to reach 1 according to the Collatz rule.
Some numbers reach the end quickly and directly.
Others rise significantly before crashing down sharply.
Some get stuck within a certain range for a while before finally reaching 1.
Each number follows a different pattern — the number of steps it takes, how high it climbs, or which values it passes through cannot be predicted in advance.
This is a structure that appears chaotic but ultimately leads to order:
Every number reaches 1, but the way it gets there is entirely uncertain.
2️⃣ How Is It Work?
The rule is simple:
▪️ If the number is even → divide it by two.
▪️ If it’s odd → multiply it by three and add one.
Repeat this process at each step.
Example :
Let’s say the starting number is 7:
7 → 22 → 11 → 34 → 17 → 52 → 26 → 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It reaches 1 in 17 steps.
And from there, it always enters the same cycle:
4 → 2 → 1 → 4 → 2 → 1...
3️⃣ Why Is It Worth Learning?
🎯 This indicator isn’t just mathematical fun—it’s a thought experiment for those who dare to question market behavior.
▪️ It’s fun.
Watching numbers behave in unpredictable ways from a simple rule set is surprisingly enjoyable.
▪️ It shows how hard it is to teach a computer what randomness really is .
The Collatz process can be used to simulate chaotic behavior and may even inspire creative ways to introduce complexity into your code.
▪️ It makes you think — especially in financial markets.
The patternless, yet rule-based structure of Collatz can help train your mind to recognize that not all unpredictability is random. It’s a great mental model for navigating complex systems like price action.
▪️ Just like price movements in financial markets, this ancient problem remains unsolved.
Despite its simplicity, the Collatz conjecture has resisted proof for decades — a reminder that even the most basic-looking systems can hide deep complexity.
4️⃣ How To Use?
Super easy — in the indicator’s settings, there’s just one input field.
Enter any positive number, and you’ll see the pattern it follows on its way to 1.
You can also observe how many steps it takes and which values it visits in the info box at the top center of the chart.
5️⃣ Some Examples
You Can Observe the Chaos in the Following Examples⤵️
For Input Number → 12
For Input Number → 13
For Input Number → 14
For Input Number → 32768
For Input Number → 47
DCA StrategyThis strategy makes it easy for you to backtest and automate the DCA strategy based on 2 triggers:
Day of the week
Every X candles
This way you can set up your DCA strategy the way you like and automate on any exchange or even a DEX, which offers an API.
The strategy is auto selling on the last candle, otherwise you won't see any performance numbers because all positions will still be open (non conclusive).
Settings
Start Date & End Date
Use those dates to help you with your backtest period. It also helps when automating, to start at a specific time to mimic what you have already done on your own portfolio and thus be in sync in TV as well.
Capital to invest per trade
Set how capital to use per DCA buy signal. Hover over the tooltip to understand, which currency is used.
Close All on last candle
When backtesting, you must close open positions, otherwise the Strategy Tester won't show you any numbers. This is why the strategy automatically closes all positions on the last candle for your convenience (ON per default).
BUT, when automating, you cannot have this checked because it would sell all of your asset on every candle open. So turn this OFF when automating.
Use Day of Week Mode
This checkbox switches between the "Day of Week" mode or the "Every X Candles" mode.
Day of Week
Opens a long position at the start of the weekday you have set it to.
Hover over the tooltip to understand, which number to use for the day of the week you need.
Every X Candles
Opens a long position after every x candles. Always at the start of every such candle.
On the daily chart, this number represents "1 day", on the 1h chart, it's "1 hour" and so on.
Properties
Initial Capital
DCA has a special quirk and that is that it invests more and more and more funds the longer it runs. But TradingView takes the Initial Capital number to calculate Net Profit, thus the Initial Capital number has to grow with every additional dollar (money) that is being invested over time, otherwise the Net Profit number will be wrong.
Sadly PineScript does not allow to set the Initial Capital number dynamically. So you have to set it manually.
To that end, this strategy shows a Label on the last candle, which shows the Invested Capital. You must take that number and put it into the Initial Capital input and click Ok .
If you don't do this, your Net Profit Number will be totally wrong!
The label must show green .
If it shows red it means you need to change the Initial Capital number before looking at the performance numbers.
After every timeframe or settings change, you must adapt the Initial Capital, otherwise you will get wrong numbers.
Follow Line Strategy Version 2.5 (React HTF)Follow Line Strategy v2.5 (React HTF) - TradingView Script Usage
This strategy utilizes a "Follow Line" concept based on Bollinger Bands and ATR to identify potential trading opportunities. It includes advanced features like optional working hours filtering, higher timeframe (HTF) trend confirmation, and improved trend-following entry/exit logic. Version 2.5 introduces reactivity to HTF trend changes for more adaptive trading.
Key Features:
Follow Line: The core of the strategy. It dynamically adjusts based on price breakouts beyond Bollinger Bands, using either the low/high or ATR-adjusted levels.
Bollinger Bands: Uses a standard Bollinger Bands setup to identify overbought/oversold conditions.
ATR Filter: Optionally uses the Average True Range (ATR) to adjust the Follow Line offset, providing a more dynamic and volatility-adjusted entry point.
Optional Trading Session Filter: Allows you to restrict trading to specific hours of the day.
Higher Timeframe (HTF) Confirmation: A significant feature that allows you to confirm trade signals with the trend on a higher timeframe. This can help to filter out false signals and improve the overall win rate.
HTF Selection Method: Choose between Auto and Manual HTF selection:
Auto: The script automatically determines the appropriate HTF based on the current chart timeframe (e.g., 1min -> 15min, 5min -> 4h, 1h -> 1D, Daily -> Monthly).
Manual: Allows you to select a specific HTF using the Manual Higher Timeframe input.
Trend-Following Entries/Exits: The strategy aims to enter trades in the direction of the established trend, using the Follow Line to define the trend.
Reactive HTF Trend Changes: v2.5 exits positions not only based on the trade timeframe (TTF) trend changing, but also when the higher timeframe trend reverses against the position. This makes the strategy more responsive to larger market movements.
Alerts: Provides buy and sell alerts for convenient trading signal notifications.
Visualizations: Plots the Follow Line for both the trade timeframe and the higher timeframe (optional), making it easy to understand the strategy's logic.
How to Use:
Add to Chart: Add the "Follow Line Strategy Version 2.5 (React HTF)" script to your TradingView chart.
Configure Settings: Customize the strategy's settings to match your trading style and preferences. Here's a breakdown of the key settings:
Indicator Settings:
ATR Period: The period used to calculate the ATR. A smaller period is more sensitive to recent price changes.
Bollinger Bands Period: The period used for the Bollinger Bands calculation. A longer period results in smoother bands.
Bollinger Bands Deviation: The number of standard deviations from the moving average that the Bollinger Bands are plotted. Higher deviations create wider bands.
Use ATR for Follow Line Offset?: Enable to use ATR to calculate the Follow Line offset. Disable to use the simple high/low.
Show Trade Signals on Chart?: Enable to show BUY/SELL labels on the chart.
Time Filter:
Use Trading Session Filter?: Enable to restrict trading to specific hours of the day.
Trading Session: The trading session to use (e.g., 0930-1600 for regular US stock market hours). Use 0000-2400 for all hours.
Higher Timeframe Confirmation:
Enable HTF Confirmation?: Enable to use the HTF trend to filter trade signals. If enabled, only trades in the direction of the HTF trend will be taken.
HTF Selection Method: Choose between "Auto" and "Manual" HTF selection.
Manual Higher Timeframe: If "Manual" is selected, choose the specific HTF (e.g., 240 for 4 hours, D for daily).
Show HTF Follow Line?: Enable to plot the HTF Follow Line on the chart.
Understanding the Signals:
Buy Signal: The price breaks above the upper Bollinger Band, and the HTF (if enabled) confirms the uptrend.
Sell Signal: The price breaks below the lower Bollinger Band, and the HTF (if enabled) confirms the downtrend.
Exit Long: The trade timeframe trend changes to downtrend or the higher timeframe trend changes to downtrend.
Exit Short: The trade timeframe trend changes to uptrend or the higher timeframe trend changes to uptrend.
Alerts:
The script includes alert conditions for buy and sell signals. To set up alerts, click the "Alerts" button in TradingView and select the desired alert condition from the script. The alert message provides the ticker and interval.
Backtesting and Optimization:
Use TradingView's Strategy Tester to backtest the strategy on different assets and timeframes.
Experiment with different settings to optimize the strategy for your specific trading style and risk tolerance. Pay close attention to the ATR Period, Bollinger Bands settings, and the HTF confirmation options.
Tips and Considerations:
HTF Confirmation: The HTF confirmation can significantly improve the strategy's performance by filtering out false signals. However, it can also reduce the number of trades.
Risk Management: Always use proper risk management techniques, such as stop-loss orders and position sizing, when trading any strategy.
Market Conditions: The strategy may perform differently in different market conditions. It's important to backtest and optimize the strategy for the specific markets you are trading.
Customization: Feel free to modify the script to suit your specific needs. For example, you could add additional filters or entry/exit conditions.
Pyramiding: The pyramiding = 0 setting prevents multiple entries in the same direction, ensuring the strategy doesn't compound losses. You can adjust this value if you prefer to pyramid into winning positions, but be cautious.
Lookahead: The lookahead = barmerge.lookahead_off setting ensures that the HTF data is calculated based on the current bar's closed data, preventing potential future peeking bias.
Trend Determination: The logic for determining the HTF trend and reacting to changes is critical. Carefully review the f_calculateHTFData function and the conditions for exiting positions to ensure you understand how the strategy responds to different market scenarios.
Disclaimer:
This script is for informational and educational purposes only. It is not financial advice, and you should not trade based solely on the signals generated by this script. Always do your own research and consult with a qualified financial advisor before making any trading decisions. The author is not responsible for any losses incurred as a result of using this script.
Smart Grid Scalping (Pullback) Strategy[BullByte]The Smart Grid Scalping (Pullback) Strategy is a high-frequency trading strategy designed for short-term traders who seek to capitalize on market pullbacks. This strategy utilizes a dynamic ATR-based grid system to define optimal entry points, ensuring precise trade execution. It integrates volatility filtering and an RSI-based confirmation mechanism to enhance signal accuracy and reduce false entries.
This strategy is specifically optimized for scalping by dynamically adjusting trade levels based on current market conditions. The grid-based system helps capture retracement opportunities while maintaining strict trade management through predefined profit targets and trailing stop-loss mechanisms.
Key Features :
1. ATR-Based Grid System :
- Uses a 10-period ATR to dynamically calculate grid levels for entry points.
- Prevents chasing trades by ensuring price has reached key levels before executing entries.
2. No Trade Zone Protection :
- Avoids low-volatility zones where price action is indecisive.
- Ensures only high-momentum trades are executed to improve success rate.
3. RSI-Based Entry Confirmation :
- Long trades are triggered when RSI is below 30 (oversold) and price is in the lower grid zone.
- Short trades are triggered when RSI is above 70 (overbought) and price is in the upper grid zone.
4. Automated Trade Execution :
- Long Entry: Triggered when price drops below the first grid level with sufficient volatility.
- Short Entry: Triggered when price exceeds the highest grid level with sufficient volatility.
5. Take Profit & Trailing Stop :
- Profit target set at a customizable percentage (default 0.2%).
- Adaptive trailing stop mechanism using ATR to lock in profits while minimizing premature exits.
6. Visual Trade Annotations :
- Clearly labeled "LONG" and "SHORT" markers appear at trade entries for better visualization.
- Grid levels are plotted dynamically to aid decision-making.
Strategy Logic :
- The script first calculates the ATR-based grid levels and ensures price action has sufficient volatility before allowing trades.
- An additional RSI filter is used to ensure trades are taken at ideal market conditions.
- Once a trade is executed, the script implements a trailing stop and predefined take profit to maximize gains while reducing risks.
---
Disclaimer :
Risk Warning :
This strategy is provided for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Users are advised to conduct their own due diligence and risk management before using this strategy in live trading.
The developer and publisher of this script are not responsible for any financial losses incurred by the use of this strategy. Market conditions, slippage, and execution quality can affect real-world trading outcomes.
Use this script at your own discretion and always trade responsibly.
Econometrica by [SS]This is Econometrica, an indicator that aims to bridge a big gap between the resources available for analysis of fundamental data and its impact on tickers and price action.
I have noticed a general dearth of available indicators that offer insight into how fundamentals impact a ticker and provide guidance on how they these economic factors influence ticker behaviour.
Enter Econometrica. Econometrica is a math based indicator that aims to co-integrate and model indicator price action in relation to critical economic metrics.
Econometrica supports the following US based economic data:
CPI
Non-Farm Payroll
Core Inflation
US Money Supply
US Central Bank Balance Sheet
GDP
PCE
Let's go over the functions of Econometrica.
Creating a Regression Cointegrated Model
The first thing Econometrica does is creates a co-integrated regression, as you see in the main chart, predicting ticker value ranges from fundamental economic data.
You can visualize this in the main chart above, but here are some other examples:
SPY vs Core Inflation:
BA vs PCE:
QQQ vs US Balance Sheet:
The band represents the anticipated range the ticker should theoretically fall in based on the underlying economic value. The indicator will breakdown the relationship between the economic indicator and the ticker more precisely. In the images above, you can see how there are some metrics provided, including Stationairty, lagged correlation, Integrated Correlation and R2. Let's discuss these very briefly:
Stationarity: checks to ensure that the relationship between the economic indicator and ticker is stationary. Stationary data is important for making unbiased inferences and projections, so having data that is stationary is valuable.
Lagged Correlation: This is a very interesting metric. Lagged correlation means whether there is a delay in the economic indicator and the response of the ticker. Typically, you will observed a lagged correlation between an economic indicator and price of a ticker, as it can take some time for economic changes to reach the market. This lagged correlation will provide you with how long it takes for the economic indicator to catch up with the ticker in months.
Integrated Correlation: This metric tells you how good of a fit the regression bands are in relation to the ticker price. A higher correlation, means the model is better at consistent and accurate information about the anticipated range for the ticker in relation to the economic indicator.
R2: Provides information on the variance and degree of model fit. A high R2 value means that the model is capable of explaining a large amount of variance between the economic indicator and the ticker price action.
Explaining the Relationship
Owning to the fact that the indicator is a bit on the mathy side (it has to be to do this kind of task), I have included ability for the indicator to explain and make suggestions based on the underlying data. It can assess the model's fit and make suggestions for tweaking. It can also explain the implications of the data being presented in the model.
Here is an example with QQQ and the US Balance Sheet:
This helps to simplify and interpret the results you are looking at.
Forecasting the Economic Indicator
In addition to assessing the economic indicator's impact on the ticker, the indicator is also capable of forecasting out the economic indicator over the next 25 releases.
Here is an example of the CPI forecast:
Overall use of the indicator
The indicator is meant to bridge the gap between Technical Analysis and Fundamental Analysis.
Any trader who is attune to fundamentals would benefit from this, as this provides you with objective data on how and to what extent fundamental and economic data impacts tickers.
It can help affirm hypothesis and dispel myths objectively.
It also omits the need from having to perform these types of analyses outside of Tradingview (i.e. in excel, R or Python), as you can get the data in just a few licks of enabling the indicator.
Conclusion
I have tried to make this indicator as user friendly as possible. Though it uses a lot of math, it is fairly straight forward to interpret.
The band plotted can be considered the fair market value or FMV of the ticker based on the underlying economic data, provided the indicator tells you that the relationship is significant (and it will blatantly give you this information verbatim, you don't have to interpret the math stuff).
This is US economic data only. It does not pull economic data from other countries. You can absolutely see how US economic data impacts other markets like the TSX, BANKNIFTY, NIFTY, DAX etc. but the indicator is only pulling US economic data.
That is it!
I hope you enjoy it and find this helpful!
Thanks everyone and safe trades as always 🚀🚀🚀
Nasan Risk Score & Postion Size Estimator** THE RISK SCORE AND POSITION SIZE WILL ONLY BE CALCUTAED ON DIALY TIMEFRAME NOT IN OTHER TIMEFRAMES.
The typically accepted generic rule for risk management is not to risk more than 1% - 2 % of the capital in any given trade. It has its own basis however it does not take into account the stocks historic & current performance and does not consider the traders performance metrics (like win rate, profit ratio).
The Nasan Risk Score & Position size calculator takes into account all the listed parameters into account and estimates a Risk %. The position size is calculated using the estimated risk % , current ATR and a dynamically adjusted ATR multiple (ATR multiple is adjusted based on true range's volatility and stocks relative performance).
It follows a series of calculations:
Unadjusted Nasan Risk Score = (Min Risk)^a + b*
Min Risk = ( 5 year weighted avg Annual Stock Return - 5 year weighted avg Annual Bench Return) / 5 year weighted avg Annual Max ATR%
Max Risk = ( 5 year weighted avg Annual Stock Return - 5 year weighted avg Annual Bench Return) / 5 year weighted avg Annual Min ATR%
The min and max return is calculated based on stocks excess return in comparison to the Benchmark return and adjusted for volatility of the stock.
When a stock underperforms the benchmark, the default is, it does not calculate a position size , however if we opt it to calculate it will use 1% for Min Risk% and 2% for Max Risk% but all the other calculations and scaling remain the same.
Rationale:
Stocks outperforming their benchmark with lower volatility (ATR%) score higher.
A stock with high returns but excessive volatility gets penalized.
This ensures volatility-adjusted performance is emphasized rather than absolute returns.
Depending on the risk preference aggressive or conservative
Aggressive Risk Scaling: a = max (m, n) and b = min (m, n)
Conservative Scaling: a = min (m, n) and b = max (m, n)
where n = traders win % /100 and m = 1 - (1/ (1+ profit ratio))
A default of 50% is used for win factor and 1.5 for profit ratio.
Aggressive risk scaling increases exposure when the strategy's strongest factor is favorable.
Conservative risk scaling ensures more stable risk levels by focusing on the weaker factor.
The Unadjusted Nasan risk is score is further refined based on a tolerance factor which is based on the stocks maximum annual drawdown and the trader's maximum draw down tolerance.
Tolerance = /100
The correction factor (Tolerance) adjusts the risk score based on downside risk. Here's how it works conceptually:
The formula calculates how much the stock's actual drawdown exceeds your acceptable limit.
If stocks maximum Annual drawdown is smaller than Trader's maximum acceptable drawdown % , this results in a positive correction factor (indicating the drawdown is within your acceptable range and increases the unadjusted score.
If stocks maximum Annual drawdown exceeds Trader's maximum acceptable drawdown %, the correction factor will decrease (indicating that the downside risk is greater than what you are comfortable with, so it will adjust the risk exposure).
Once the Risk Score (numerically equal to Risk %) The position size is calculated based on the current market conditions.
Nasan Risk Score (Risk%) = Unadjusted Nasan Risk Score * Tolerance.
Position Size = (Capital * Risk% )/ ATR-Multiplier * ATR
The ATR Multiplier is dynamically adjusted based on the stocks recent relative performance and the variability of the true range itself. It would range between 1 - 3.5.
The multiplier widens when conditions are not favorable decreasing the position size and increases position size when conditions are favorable.
This Calculation /Estimate Does not give you a very different result than the arbitrary 1% - 2%. However it does fine tune the % based on sock performance, traders performance and tolerance level.
Dynamic Trend Indicator (DTI) - VWAP FilterThe Dynamic Trend Indicator (DTI) with VWAP Filter is a trend-following indicator.
It aims to identify and follow market trends while minimizing false signals in choppy or ranging markets.
The DTI combines a dynamically adjusted Exponential Moving Average (EMA) with a daily Volume Weighted Average Price (VWAP) confirmation filter and a cooldown mechanism to enhance signal reliability. This indicator is particularly useful for traders on intraday timeframes (e.g., 4-hour charts) who want to align their trades with the broader daily trend while avoiding whipsaws.
Key Features:
Dynamic Trend Line:
The core of the DTI is a trend line calculated using a custom EMA that adjusts its period dynamically based on market conditions.
The period of the EMA is determined by a combination of volatility (measured via ATR) and trend strength (measured via price momentum). In strong trends, the period shortens for faster responsiveness; in weak or ranging markets, it lengthens to reduce noise.
An optional smoothing EMA can be applied to the dynamic trend line to further reduce noise, with a user-defined smoothing length.
Daily VWAP Confirmation Filter:
A daily VWAP is calculated to provide a higher-timeframe trend bias. VWAP represents the average price paid for an asset during the day, weighted by volume, and is often used as a benchmark by institutional traders.
Buy signals are only generated when the price is above the daily VWAP (indicating a bullish daily bias), and sell signals are only generated when the price is below the VWAP (indicating a bearish daily bias).
The VWAP resets at the start of each day, ensuring it reflects the current day’s trading activity.
Cooldown Mechanism:
To prevent rapid signal reversals (whipsaws), the indicator includes a cooldown period between signals. After a buy or sell signal is generated, no new signals can be generated for a user-defined number of bars (default: 5 bars).
This helps filter out noise in choppy markets, ensuring signals are spaced out and more likely to align with significant trend changes.
Visual Elements:
Trend Line: Plotted on the chart, colored green when the price is above (uptrend) and red when below (downtrend). A gray color indicates a neutral trend.
Buy/Sell Signals: Displayed as green triangles below the bar for buy signals and red triangles above the bar for sell signals.
Background Coloring: The chart background is shaded green during uptrends and red during downtrends, providing a quick visual cue of the trend direction.
Daily VWAP Line: Optionally plotted as a purple step line, allowing traders to see the VWAP level and its relationship to the price.
Alerts:
The indicator includes built-in alerts for buy and sell signals, triggered when the price crosses the trend line and satisfies the VWAP filter and cooldown conditions.
Alert messages specify whether the signal is a buy or sell and confirm that the VWAP condition was met (e.g., "DTI Buy Signal: Price crossed above trend line and VWAP").
Input Parameters
Base Length (default: 14): The base period for calculating volatility and trend strength, used to adjust the dynamic EMA period.
Volatility Multiplier (default: 1.5): Adjusts the sensitivity of the dynamic period to market volatility (via ATR).
Trend Threshold (default: 0.5): Controls the sensitivity of the dynamic period to trend strength (via price momentum).
Use Smoothing (default: true): Enables/disables smoothing of the trend line with an additional EMA.
Smoothing Length (default: 3): The period for the smoothing EMA, if enabled.
Cooldown Bars (default: 5): The minimum number of bars between consecutive signals, reducing signal frequency in choppy markets.
Show Daily VWAP (default: true): Toggles the display of the daily VWAP line on the chart.
How It Works
Dynamic Trend Line Calculation:
Volatility is measured using the Average True Range (ATR) over the base length, scaled by the volatility multiplier.
Trend strength is calculated as the absolute price momentum (change in price over the base length) divided by the volatility factor.
The dynamic EMA period is adjusted based on the trend strength: stronger trends result in a shorter period (faster response), while weaker trends result in a longer period (more stability). The period is constrained between 5 and 50 to avoid extreme values.
A custom EMA function is used to handle the dynamic period, as Pine Script’s built-in ta.ema() requires a fixed length. The trend line is optionally smoothed with a secondary EMA.
Signal Generation:
A buy signal is generated when the price crosses above the trend line, the price is above the daily VWAP, and the cooldown period has elapsed.
A sell signal is generated when the price crosses below the trend line, the price is below the daily VWAP, and the cooldown period has elapsed.
The cooldown mechanism ensures that signals are not generated too frequently, reducing false signals in ranging markets.
Daily VWAP Calculation:
The VWAP is calculated by accumulating the price-volume product (close * volume) and total volume for the day, resetting at the start of each new day.
The VWAP is then computed as the cumulative price-volume divided by the cumulative volume, providing a volume-weighted average price for the day.
Usage
Timeframe: Best suited for intraday timeframes (e.g., 1-hour, 4-hour) where the daily VWAP provides a higher-timeframe trend bias. It can also be used on daily charts with adjustments to the cooldown period.
Markets: Works well in trending markets (e.g., forex, crypto, stocks) where the dynamic trend line can capture sustained price movements. The VWAP filter helps align signals with the daily trend, making it effective for assets with clear daily biases.
Trading Strategy:
Buy: Enter a long position when a green triangle (buy signal) appears, indicating the price has crossed above the trend line and is above the daily VWAP.
Sell: Enter a short position (or exit a long) when a red triangle (sell signal) appears, indicating the price has crossed below the trend line and is below the daily VWAP.
Use the trend line and VWAP as dynamic support/resistance levels to set stop-losses or take-profit targets.
Backtesting: Use TradingView’s strategy tester to evaluate the indicator’s performance on your chosen market and timeframe, adjusting parameters like cooldown_bars and volatility_mult to optimize for profitability.
Example
On a 4-hour SOLUSDT chart, the DTI with VWAP Filter might show:
An uptrend with the price above the green trend line and above the daily VWAP, generating buy signals as the price continues to rise.
A downtrend where the price falls below the red trend line and the daily VWAP, generating sell signals that align with the bearish daily bias.
During choppy periods, the cooldown mechanism and VWAP filter reduce false signals, ensuring trades are taken only when the price aligns with the daily trend.
Limitations
Lagging Nature: Like all trend-following indicators, the DTI may lag during sharp price reversals, as the dynamic EMA needs time to adjust.
Ranging Markets: While the VWAP filter and cooldown mechanism reduce whipsaws, the indicator may still generate some false signals in strongly ranging markets. Combining it with a trend strength filter (e.g., ADX) can help.
VWAP Dependency: The effectiveness of the VWAP filter depends on the market’s respect for the daily VWAP as a support/resistance level. In markets with low volume or erratic price action, the VWAP may be less reliable.
Potential Improvements
VWAP Buffer: Add a percentage buffer around the VWAP (e.g., require the price to be 1% above/below) to further reduce noise.
Multi-Timeframe VWAP: Incorporate a weekly VWAP for additional trend confirmation on longer timeframes.
Trend Strength Filter: Add an ADX filter to ensure signals are generated only during strong trends (e.g., ADX > 25).
Session Profile AnalyzerWhat’s This Thing Do?
Hey there, trader! Meet the Session Profile Analyzer (SPA) your new go-to pal for breaking down market action within your favorite trading sessions. It’s an overlay indicator that mixes Rotation Factor (RF), Average Subperiod Range (ASPR), Volume Value Area Range (VOLVAR), and TPO Value Area Range (TPOVAR) into one tidy little toolkit. Think of it as your market vibe checker momentum, volatility, and key levels, all served up with a grin.
The Cool Stuff It Does:
Rotation Factor (RF) : Keeps tabs on whether the market’s feeling bullish, bearish, or just chilling. It’s like a mood ring for price action shows “UP ↑,” “DOWN ↓,” or “NONE ↔.”
ASPR : Averages out the range of your chosen blocks. Big swings? Tiny wiggles? This tells you the session’s energy level.
VOLVAR : Dives into volume to find where the action’s at, with a smart twist it adjusts price levels based on the session’s size and tiny timeframe moves (capped at 128 so your chart doesn’t cry).
TPOVAR : Grabs lower timeframe data to spot where price hung out the most, TPO-style. Value zones, anyone?
Dynamic Precision : No ugly decimal overload SPA matches your asset’s style (2 decimals for BTC, 5 for TRX, you get it).
How to Play With It:
Session Start/End : Pick your trading window (say, 0930-2200) and a timezone (America/New_York, or wherever you’re at).
Block Size : Set the chunk size for RF and ASPR like 30M if you’re into half-hour vibes.
Value Area Timeframe : Go micro with something like 1S for VOLVAR and TPOVAR precision.
Label : Size it (small to huge), color it (white, neon pink, whatever), and slap it where you want (start, mid, end).
How It All Works (No PhD Required):
RF : Imagine breaking your session into blocks (via Block Size). For each block, SPA checks if the high beats the last high (+1) or not (0), and if the low dips below the last low (-1) or not (0). Add those up, and boom positive RF means upward vibes, negative means downward, near zero is “meh.” Use it to catch trends or spot when the market’s napping.
ASPR : Takes those same blocks, measures high-to-low range each time, and averages them. It’s your volatility pulse big ASPR = wild ride, small ASPR = snooze fest. Great for sizing up session action.
VOLVAR : Here’s the fun part. It takes the session’s full range (high minus low), divides it by the average range of your tiny Value Area Timeframe bars (e.g., 1S), and picks a sensible number of price levels capped at 128 so it doesn’t overthink. Then it bins volume into those levels, finds the busiest price (POC), and grows a 70% value area around it. Perfect for spotting where the big players parked their cash.
TPOVAR : Grabs midpoints from those tiny timeframe bars, sorts them, and snips off the top and bottom 15% to find the 70% “value zone” where price chilled the most. Think of it as the market’s comfort zone great for support/resistance hunting.
Why You’ll Like It:
Whether you’re scalping crypto, swinging forex, or dissecting stocks, SPA’s got your back. Use RF to catch momentum shifts like jumping on an “UP ↑” trend or fading a “DOWN ↓” exhaustion. ASPR’s your secret weapon for sizing up trades: a big ASPR (say, 100 on BTC) means you can aim for juicy targets (like 1-2x ASPR) or set invalidations tight when it’s tiny (e.g., 0.001 on TRX) to dodge chop. VOLVAR and TPOVAR are your level-finders nail those key zones where price loves to bounce or break, perfect for entries, stops, or profit grabs. It’s like having a trading co-pilot who’s chill but knows their stuff.
Heads-Up:
Load enough history for those micro timeframes to shine (1S needs some bars to work with).
Keeps things light won’t bog down your chart even with decent-sized sessions.
Let’s Roll:
Slap SPA on your chart, tweak it to your style, and watch it spill the beans on your session. Happy trading, fam may your pips be plenty and your losses few!
Hurst-Based Trend Persistence w/Poisson Prediction
---
# **Hurst-Based Trend Persistence w/ Poisson Prediction**
## **Introduction**
The **Hurst-Based Trend Persistence with Poisson Prediction** is a **statistically-driven trend-following oscillator** that provides traders with **a structured approach to identifying trend strength, persistence, and potential reversals**.
This indicator combines:
- **Hurst Exponent Analysis** (to measure how persistent or mean-reverting price action is).
- **Color-Coded Trend Detection** (to highlight bullish and bearish conditions).
- **Poisson-Based Trend Reversal Probability Projection** (to anticipate when a trend is likely to end based on statistical models).
By integrating **fractal market theory (Hurst exponent)** with **Poisson probability distributions**, this indicator gives traders a **probability-weighted view of trend duration** while dynamically adapting to market volatility.
---
## **Simplified Explanation (How to Read the Indicator at a Glance)**
1. **If the oscillator line is going up → The trend is strong.**
2. **If the oscillator line is going down → The trend is weakening.**
3. **If the color shifts from red to green (or vice versa), a trend shift has occurred.**
- **Strong trends can change color without weakening** (meaning a bullish or bearish move can remain powerful even as the trend shifts).
4. **A weakening trend does NOT necessarily mean a reversal is coming.**
- The trend may slow down but continue in the same direction.
5. **A strong trend does NOT guarantee it will last.**
- Even a powerful move can **suddenly reverse**, which is why the **Poisson-based background shading** helps anticipate probabilities of change.
---
## **How to Use the Indicator**
### **1. Understanding the Rolling Hurst-Based Trend Oscillator (Main Line)**
The **oscillator line** is based on the **Hurst exponent (H)**, which quantifies whether price movements are:
- **Trending** (values above 0 → momentum-driven, persistent trends).
- **Mean-reverting** (values below 0 → price action is choppy, likely to revert to the mean).
- **Neutral (Random Walk)** (values around 0 → price behaves like a purely stochastic process).
#### **Interpreting the Oscillator:**
- **H > 0.5 → Persistent Trends:**
- Price moves tend to sustain in one direction for longer periods.
- Example: Strong uptrends in bull markets.
- **H < 0.5 → Mean-Reverting Behavior:**
- Price has a tendency to revert back to its mean.
- Example: Sideways markets or fading momentum.
- **H ≈ 0.5 → Random Walk:**
- No clear trend; price is unpredictable.
A **gray dashed horizontal line at 0** serves as a **baseline**, helping traders quickly assess whether the market is **favoring trends or mean reversion**.
---
### **2. Color-Coded Trend Signal (Visual Confirmation of Trend Shifts)**
The oscillator **changes color** based on **price slope** over the lookback period:
- **🟢 Green → Uptrend (Price Increasing)**
- Price is rising relative to the selected lookback period.
- Suggests sustained bullish pressure.
- **🔴 Red → Downtrend (Price Decreasing)**
- Price is falling relative to the selected lookback period.
- Suggests sustained bearish pressure.
#### **How to Use This in Trading**
✔ **Stay in trends until a color change occurs.**
✔ **Use color changes as confirmation for trend reversals.**
✔ **Avoid counter-trend trades when the oscillator remains strongly colored.**
---
### **3. Poisson-Based Trend Reversal Projection (Anticipating Future Shifts)**
The **shaded orange background** represents a **Poisson-based probability estimation** of when the trend is likely to reverse.
- **Darker Orange = Higher Probability of Trend Reversal**
- **Lighter Orange / No Shade = Low Probability of Immediate Reversal**
💡 **The idea behind this model:**
✔ Trends **don’t last forever**, and their duration follows **statistical patterns**.
✔ By calculating the **average historical trend duration**, the indicator predicts **how likely a trend shift is at any given time**.
✔ The **Poisson probability function** is applied to determine the **expected likelihood of a reversal as time progresses**.
---
## **Mathematical Foundations of the Indicator**
This indicator is based on **two primary statistical models**:
### **1. Hurst Exponent & Trend Persistence (Fractal Market Theory)**
- The **Hurst exponent (H)** measures **autocorrelation** in price movements.
- If past trends **persist**, H will be **above 0.5** (meaning trend-following strategies are favorable).
- If past trends tend to **mean-revert**, H will be **below 0.5** (meaning reversal strategies are more effective).
- The **Rolling Hurst Oscillator** calculates this exponent over a moving window to track real-time trend conditions.
#### **Formula Breakdown (Simplified for Traders)**
The Hurst exponent (H) is derived using the **Rescaled Range (R/S) Analysis**:
\
Where:
- **R** = **Range** (difference between max cumulative deviation and min cumulative deviation).
- **S** = **Standard deviation** of price fluctuations.
- **Lookback** = The number of periods analyzed.
---
### **2. Poisson-Based Trend Reversal Probability (Stochastic Process Modeling)**
The **Poisson process** is a **probabilistic model used for estimating time-based events**, applied here to **predict trend reversals based on past trend durations**.
#### **How It Works**
- The indicator **tracks trend durations** (the time between color changes).
- A **Poisson rate parameter (λ)** is computed as:
\
- The **probability of a reversal at any given time (t)** is estimated using:
\
- **As t increases (trend continues), the probability of reversal rises**.
- The indicator **shades the background based on this probability**, visually displaying the likelihood of a **trend shift**.
---
## **Dynamic Adaptation to Market Conditions**
✔ **Volatility-Adjusted Trend Shifts:**
- A **custom volatility calculation** dynamically adjusts the **minimum trend duration** required before a trend shift is recognized.
- **Higher volatility → Requires longer confirmation before switching trend color.**
- **Lower volatility → Allows faster trend shifts.**
✔ **Adaptive Poisson Weighting:**
- **Recent trends are weighted more heavily** using an exponential decay function:
- **Decay Factor (0.618 by default)** prioritizes **recent intervals** while still considering historical trends.
- This ensures the model adapts to changing market conditions.
---
## **Key Takeaways for Traders**
✅ **Identify Persistent Trends vs. Mean Reversion:**
- Use the oscillator line to determine whether the market favors **trend-following or counter-trend strategies**.
✅ **Visual Trend Confirmation via Color Coding:**
- **Green = Uptrend**, **Red = Downtrend**.
- Trend changes help confirm **entry and exit points**.
✅ **Anticipate Trend Reversals Using Probability Models:**
- The **Poisson projection** provides a **statistical edge** in **timing exits before trends reverse**.
✅ **Adapt to Market Volatility Automatically:**
- Dynamic **volatility scaling** ensures the indicator remains effective in **both high and low volatility environments**.
Happy trading and enjoy!
BTCUSD with adjustable sl,tpThis strategy is designed for swing traders who want to enter long positions on pullbacks after a short-term trend shift, while also allowing immediate short entries when conditions favor downside movement. It combines SMA crossovers, a fixed-percentage retracement entry, and adjustable risk management parameters for optimal trade execution.
Key Features:
✅ Trend Confirmation with SMA Crossover
The 10-period SMA crossing above the 25-period SMA signals a bullish trend shift.
The 10-period SMA crossing below the 25-period SMA signals a bearish trend shift.
Short trades are only taken if the price is below the 150 EMA, ensuring alignment with the broader trend.
📉 Long Pullback Entry Using Fixed Percentage Retracement
Instead of entering immediately on the SMA crossover, the strategy waits for a retracement before going long.
The pullback entry is defined as a percentage retracement from the recent high, allowing for an optimized entry price.
The retracement percentage is fully adjustable in the settings (default: 1%).
A dynamic support level is plotted on the chart to visualize the pullback entry zone.
📊 Short Entry Rules
If the SMA(10) crosses below the SMA(25) and price is below the 150 EMA, a short trade is immediately entered.
Risk Management & Exit Strategy:
🚀 Take Profit (TP) – Fully customizable profit target in points. (Default: 1000 points)
🛑 Stop Loss (SL) – Adjustable stop loss level in points. (Default: 250 points)
🔄 Break-Even (BE) – When price moves in favor by a set number of points, the stop loss is moved to break-even.
📌 Extra Exit Condition for Longs:
If the SMA(10) crosses below SMA(25) while the price is still below the EMA150, the strategy force-exits the long position to avoid reversals.
How to Use This Strategy:
Enable the strategy on your TradingView chart (recommended for stocks, forex, or indices).
Customize the settings – Adjust TP, SL, BE, and pullback percentage for your risk tolerance.
Observe the plotted retracement levels – When the price touches and bounces off the level, a long trade is triggered.
Let the strategy manage the trade – Break-even protection and take-profit logic will automatically execute.
Ideal Market Conditions:
✅ Trending Markets – The strategy works best when price follows strong trends.
✅ Stocks, Indices, or Forex – Can be applied across multiple asset classes.
✅ Medium-Term Holding Period – Suitable for swing trades lasting days to weeks.
Normalized VolumeOVERVIEW
The Normalized Volume (NV) is an attempt at visualizing volume in a format that is more understandable by placing the values on a scale of 0 to 100. 0 in this case is the lowest volume candle available on the chart, and 100 being the highest. Calling a candle “high volume” can be misleading without having something to compare to. For example, in scaling the volume this way we can clearly see that a given candle had 80% of the peak volume or 20%, and gauge the validity of price moves more accurately.
FEATURES
NV by session
Allows user to filter the volume values across 4 different sessions. This can add context to the volume output, because what it high volume during London session may not be high volume relative to New York session.
Overlay plotting
When volume boxes are turned on, this will allow you to toggle how they are plotted.
Color theme
A standard color theme will color the NV based on if the respective candle closed green or red. Selecting variables will color the NV plot based on which range the value falls within.
Session inputs
Activated with the “By session?” Input. Allows user to break the day up into 4 sessions to more accurately gauge volume relative to time of day.
Show Box (X)
Toggles on chart boxes on and off.
Show historical boxes
Will plot prior occurrences of selected volume boxes, deleting them when price fully moves through them in the opposite direction of the initial candle.
Color inputs
Allows for intensive customization in how this tool appears visually.
INTERPRETATION
There are 6 pre-defined ranges that NV can fall within.
NV <= 10
Volume is insignificant
In this range, volume should not be a confirmation in your trading strategy.
NV > 10 and <= 20
Volume is low
In this range, volume should not be a confirmation in your trading strategy.
NV > 20 and <= 40
Volume is fair
In this range, volume should not be the primary confirmation in your trading strategy.
NV > 40 and <= 60
Volume is high
In this range, volume can be the primary confirmation in your trading strategy.
NV > 60 and <= 80
Volume is very high
In this range, volume can be the primary confirmation in your trading strategy.
NV > 80
Volume is extreme
In this range, volume is likely news driven and caution should be taken. High price volatility possible.
To utilize this tool in conjunction with your current strategy, follow the range explanations above section in this section. The higher the NV value, the stronger you can feel about your directional confirmation.
If NV = 100, this means that the highest volume candle occurred up to that point on your selected timeframe. All future data points will be weighed off of this value.
LIMITATIONS
This tool will not load on tickers that do not have volume data, such as VIX.
STRATEGY
The Normalized Volume plot can be used in exactly the same way as you would normally utilize volume in your trading strategy. All we are doing is weighing the volume relative to itself.
Volume boxes can be used as targets to be filled in a similar way to commonly used “fair value gap” strategies. To utilize this strategy, I recommend selecting “Plot to Wicks” in Overlay Plotting and toggling on Show Historical Boxes.
Volume boxes can be used as areas for entry in a similar way to commonly used “order block” strategies. To utilize this strategy, I recommend selecting “Open To Close” in Overlay Plotting.
NOTES
You are able to plot an info label on right side of NV plot using the "Toggle box label" input. When a box is toggled on this label will tell you when the most recent box of that intensity occurred.
This tool is deeply visually customizable, with the ability to adjust line width for plotted boxes, all colors on both box overlays, and all colors on NV panel. Customize it to your liking!
I have a handful of additional features that I plan on adding to this tool in future updates. If there is anything you would like to see added, any bugs you identify, or any strategies you encounter with this tool, I would love to hear from you!
Huge shoutout to @joebaus for assisting in bringing this tool to life, please check out his work here on TradingView!
Premarket Gap MomoTrader(SC)🚀 Pre-Market Momentum Trader | Dynamic Position Sizing 🔥
📈 Trade explosive pre-market breakouts with confidence! This algorithmic strategy automatically detects high-momentum setups, dynamically adjusts position size, and ensures risk control with a one-trade-per-day rule.
⸻
🎯 Key Features
✅ Pre-Market Trading (4:00 - 9:30 AM EST) – Only trades during the most volatile session for early breakouts.
✅ Dynamic Position Sizing – Adapts trade size based on candle strength:
• ≥90% body → 100% position
• ≥85% body → 50% position
• ≥75% body → 25% position
✅ 1 Trade Per Day – Avoids overtrading by allowing only one high-quality trade daily.
✅ Momentum Protection – Stays in the trade as long as:
• Every candle remains green (no red candles).
• Each new candle has increasing volume (confirming strong buying).
✅ Automated Exit – Closes position if:
• A red candle appears.
• Volume fails to increase on a green candle.
⸻
🔍 How It Works
📌 Entry Conditions:
✔️ Candle gains ≥5% from previous close.
✔️ Candle is green & body size ≥75% of total range.
✔️ Volume >15K (confirming liquidity).
✔️ Occurs within pre-market session (4:00 - 9:30 AM EST).
✔️ Only the first valid trade of the day is taken.
📌 Exit Conditions:
❌ First red candle after entry → Exit trade.
❌ First green candle with lower volume → Exit trade.
⸻
🏆 Why Use This?
🔹 Eliminates Fake Breakouts – No trade unless volume & momentum confirm.
🔹 Prevents Overtrading – Restricts to one quality trade per day.
🔹 Adaptable to Any Market – Works on stocks, crypto, or forex.
🔹 Hands-Free Execution – No manual chart watching required!
⸻
🚨 Important Notes
📢 Not financial advice. Trading involves risk—always backtest & practice on paper trading before using real money.
📢 Enable pre-market data in your TradingView settings for accurate results.
📢 Optimized for 1-minute & 5-minute timeframes.
🔔 Like this strategy? Leave a comment, share your results, and don’t forget to hit Follow for more strategies! 🚀🔥
Liquidity Sweep Filter Strategy [AlgoAlpha X PineIndicators]This strategy is based on the Liquidity Sweep Filter developed by AlgoAlpha. Full credit for the concept and original indicator goes to AlgoAlpha.
The Liquidity Sweep Filter Strategy is a non-repainting trading system designed to identify liquidity sweeps, trend shifts, and high-impact price levels. It incorporates volume-based liquidation analysis, trend confirmation, and dynamic support/resistance detection to optimize trade entries and exits.
This strategy helps traders:
Detect liquidity sweeps where major market participants trigger stop losses and liquidations.
Identify trend shifts using a volatility-based moving average system.
Analyze volume distribution with a built-in volume profile visualization.
Filter noise by differentiating between major and minor liquidity sweeps.
How the Liquidity Sweep Filter Strategy Works
1. Trend Detection Using Volatility-Based Filtering
The strategy applies a volatility-adjusted moving average system to determine trend direction:
A central trend line is calculated using an EMA smoothed over a user-defined length.
Upper and lower deviation bands are created based on the average price deviation over multiple periods.
If price closes above the upper band, the strategy signals an uptrend.
If price closes below the lower band, the strategy signals a downtrend.
This approach ensures that trend shifts are confirmed only when price significantly moves beyond normal market fluctuations.
2. Liquidity Sweep Detection
Liquidity sweeps occur when price temporarily breaks key levels, triggering stop-loss liquidations or margin call events. The strategy tracks swing highs and lows, marking potential liquidity grabs:
Bearish Liquidity Sweeps – Price breaks a recent high, then reverses downward.
Bullish Liquidity Sweeps – Price breaks a recent low, then reverses upward.
Volume Integration – The strategy analyzes trading volume at each sweep to differentiate between major and minor sweeps.
Key levels where liquidity sweeps occur are plotted as color-coded horizontal lines:
Red lines indicate bearish liquidity sweeps.
Green lines indicate bullish liquidity sweeps.
Labels are displayed at each sweep, showing the volume of liquidated positions at that level.
3. Volume Profile Analysis
The strategy includes an optional volume profile visualization, displaying how trading volume is distributed across different price levels.
Features of the volume profile:
Point of Control (POC) – The price level with the highest traded volume is marked as a key area of interest.
Bounding Box – The profile is enclosed within a transparent box, helping traders visualize the price range of high trading activity.
Customizable Resolution & Scale – Traders can adjust the granularity of the profile to match their preferred time frame.
The volume profile helps identify zones of strong support and resistance, making it easier to anticipate price reactions at key levels.
Trade Entry & Exit Conditions
The strategy allows traders to configure trade direction:
Long Only – Only takes long trades.
Short Only – Only takes short trades.
Long & Short – Trades in both directions.
Entry Conditions
Long Entry:
A bullish trend shift is confirmed.
A bullish liquidity sweep occurs (price sweeps below a key level and reverses).
The trade direction setting allows long trades.
Short Entry:
A bearish trend shift is confirmed.
A bearish liquidity sweep occurs (price sweeps above a key level and reverses).
The trade direction setting allows short trades.
Exit Conditions
Closing a Long Position:
A bearish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Closing a Short Position:
A bullish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Customization Options
The strategy offers multiple adjustable settings:
Trade Mode: Choose between Long Only, Short Only, or Long & Short.
Trend Calculation Length & Multiplier: Adjust how trend signals are calculated.
Liquidity Sweep Sensitivity: Customize how aggressively the strategy identifies sweeps.
Volume Profile Display: Enable or disable the volume profile visualization.
Bounding Box & Scaling: Control the size and position of the volume profile.
Color Customization: Adjust colors for bullish and bearish signals.
Considerations & Limitations
Liquidity sweeps do not always result in reversals. Some price sweeps may continue in the same direction.
Works best in volatile markets. In low-volatility environments, liquidity sweeps may be less reliable.
Trend confirmation adds a slight delay. The strategy ensures valid signals, but this may result in slightly later entries.
Large volume imbalances may distort the volume profile. Adjusting the scale settings can help improve visualization.
Conclusion
The Liquidity Sweep Filter Strategy is a volume-integrated trading system that combines liquidity sweeps, trend analysis, and volume profile data to optimize trade execution.
By identifying key price levels where liquidations occur, this strategy provides valuable insight into market behavior, helping traders make better-informed trading decisions.
Key use cases for this strategy:
Liquidity-Based Trading – Capturing moves triggered by stop hunts and liquidations.
Volume Analysis – Using volume profile data to confirm high-activity price zones.
Trend Following – Entering trades based on confirmed trend shifts.
Support & Resistance Trading – Using liquidity sweep levels as dynamic price zones.
This strategy is fully customizable, allowing traders to adapt it to different market conditions, timeframes, and risk preferences.
Full credit for the original concept and indicator goes to AlgoAlpha.
IU Gap Fill StrategyThe IU Gap Fill Strategy is designed to capitalize on price gaps that occur between trading sessions. It identifies gaps based on a user-defined percentage threshold and executes trades when the price fills the gap within a day. This strategy is ideal for traders looking to take advantage of market inefficiencies that arise due to overnight or session-based price movements. An ATR-based trailing stop-loss is incorporated to dynamically manage risk and lock in profits.
USER INPUTS
Percentage Difference for Valid Gap - Defines the minimum gap size in percentage terms for a valid trade setup. ( Default is 0.2 )
ATR Length - Sets the lookback period for the Average True Range (ATR) calculation. (default is 14 )
ATR Factor - Determines the multiplier for the trailing stop-loss, helping in risk management. ( Default is 2.00 )
LONG CONDITION
A gap-up occurs, meaning the current session opens above the previous session’s close.
The price initially dips below the previous session's close but then recovers and closes above it.
The gap meets the valid percentage threshold set by the user.
The bar is not the first or last bar of the session to avoid false signals.
SHORT CONDITION
A gap-down occurs, meaning the current session opens below the previous session’s close.
The price initially moves above the previous session’s close but then closes below it.
The gap meets the valid percentage threshold set by the user.
The bar is not the first or last bar of the session to avoid false signals.
LONG EXIT
An ATR-based trailing stop-loss is set below the entry price and dynamically adjusts upwards as the price moves in favor of the trade.
The position is closed when the trailing stop-loss is hit.
SHORT EXIT
An ATR-based trailing stop-loss is set above the entry price and dynamically adjusts downwards as the price moves in favor of the trade.
The position is closed when the trailing stop-loss is hit.
WHY IT IS UNIQUE
Precision in Identifying Gaps - The strategy focuses on real price gaps rather than minor fluctuations.
Dynamic Risk Management - Uses ATR-based trailing stop-loss to secure profits while allowing the trade to run.
Versatility - Works on stocks, indices, forex, and any market that experiences session-based gaps.
Optimized Entry Conditions - Ensures entries are taken only when the price attempts to fill the gap, reducing false signals.
HOW USERS CAN BENEFIT FROM IT
Enhance Trade Timing - Captures high-probability trade setups based on market inefficiencies caused by gaps.
Minimize Risk - The ATR trailing stop-loss helps protect gains and limit losses.
Works in Different Market Conditions - Whether markets are trending or consolidating, the strategy adapts to potential gap fill opportunities.
Fully Customizable - Users can fine-tune gap percentage, ATR settings, and stop-loss parameters to match their trading style.
Dual SuperTrend w VIX Filter - Strategy [presentTrading]Hey everyone! Haven't been here for a long time. Been so busy again in the past 2 months. I recently started working on analyzing the combination of trend strategy and VIX, but didn't get outstanding results after a few tries. Sharing this tool with all of you in case you have better insights.
█ Introduction and How it is Different
The Dual SuperTrend with VIX Filter Strategy combines traditional trend following with market volatility analysis. Unlike conventional SuperTrend strategies that focus solely on price action, this experimental system incorporates VIX (Volatility Index) as an adaptive filter to create a more context-aware trading approach. By analyzing where current volatility stands relative to historical norms, the strategy adjusts to different market environments rather than applying uniform logic across all conditions.
BTCUSD 6hr Long Short Performance
█ Strategy, How it Works: Detailed Explanation
🔶 Dual SuperTrend Core
The strategy uses two SuperTrend indicators with different sensitivity settings:
- SuperTrend 1: Length = 13, Multiplier = 3.5
- SuperTrend 2: Length = 8, Multiplier = 5.0
The SuperTrend calculation follows this process:
1. ATR = Average of max(High-Low, |High-PreviousClose|, |Low-PreviousClose|) over 'length' periods
2. UpperBand = (High+Low)/2 - (Multiplier * ATR)
3. LowerBand = (High+Low)/2 + (Multiplier * ATR)
Trend direction is determined by:
- If Close > previous LowerBand, Trend = Bullish (1)
- If Close < previous UpperBand, Trend = Bearish (-1)
- Otherwise, Trend = previous Trend
🔶 VIX Analysis Framework
The core innovation lies in the VIX analysis system:
1. Statistical Analysis:
- VIX Mean = SMA(VIX, 252)
- VIX Standard Deviation = StdDev(VIX, 252)
- VIX Z-Score = (Current VIX - VIX Mean) / VIX StdDev
2. **Volatility Bands:
- Upper Band 1 = VIX Mean + (2 * VIX StdDev)
- Upper Band 2 = VIX Mean + (3 * VIX StdDev)
- Lower Band 1 = VIX Mean - (2 * VIX StdDev)
- Lower Band 2 = VIX Mean - (3 * VIX StdDev)
3. Volatility Regimes:
- "Very Low Volatility": VIX < Lower Band 1
- "Low Volatility": Lower Band 1 ≤ VIX < Mean
- "Normal Volatility": Mean ≤ VIX < Upper Band 1
- "High Volatility": Upper Band 1 ≤ VIX < Upper Band 2
- "Extreme Volatility": VIX ≥ Upper Band 2
4. VIX Trend Detection:
- VIX EMA = EMA(VIX, 10)
- VIX Rising = VIX > VIX EMA
- VIX Falling = VIX < VIX EMA
Local performance:
🔶 Entry Logic Integration
The strategy combines trend signals with volatility filtering:
Long Entry Condition:
- Both SuperTrend 1 AND SuperTrend 2 must be bullish (trend = 1)
- AND selected VIX filter condition must be satisfied
Short Entry Condition:
- Both SuperTrend 1 AND SuperTrend 2 must be bearish (trend = -1)
- AND selected VIX filter condition must be satisfied
Available VIX filter rules include:
- "Below Mean + SD": VIX < Lower Band 1
- "Below Mean": VIX < VIX Mean
- "Above Mean": VIX > VIX Mean
- "Above Mean + SD": VIX > Upper Band 1
- "Falling VIX": VIX < VIX EMA
- "Rising VIX": VIX > VIX EMA
- "Any": No VIX filtering
█ Trade Direction
The strategy allows testing in three modes:
1. **Long Only:** Test volatility effects on uptrends only
2. **Short Only:** Examine volatility's impact on downtrends only
3. **Both (Default):** Compare how volatility affects both trend directions
This enables comparative analysis of how volatility regimes impact bullish versus bearish markets differently.
█ Usage
Use this strategy as an experimental framework:
1. Form a hypothesis about how volatility affects trend reliability
2. Configure VIX filters to test your specific hypothesis
3. Analyze performance across different volatility regimes
4. Compare results between uptrends and downtrends
5. Refine your volatility filtering approach based on results
6. Share your findings with the trading community
This framework allows you to investigate questions like:
- Are uptrends more reliable during rising or falling volatility?
- Do downtrends perform better when volatility is above or below its historical average?
- Should different volatility filters be applied to long vs. short positions?
█ Default Settings
The default settings serve as a starting point for exploration:
SuperTrend Parameters:
- SuperTrend 1 (Length=13, Multiplier=3.5): More responsive to trend changes
- SuperTrend 2 (Length=8, Multiplier=5.0): More selective filter requiring stronger trends
VIX Analysis Settings:
- Lookback Period = 252: Establishes a full market cycle for volatility context
- Standard Deviation Bands = 2 and 3 SD: Creates statistically significant regime boundaries
- VIX Trend Period = 10: Balances responsiveness with noise reduction
Default VIX Filter Selection:
- Long Entry: "Above Mean" - Tests if uptrends perform better during above-average volatility
- Short Entry: "Rising VIX" - Tests if downtrends accelerate when volatility is increasing
Feel Free to share your insight below!!!
Shavarie's MCV IndicatorShavarie's MCV Indicator (MACD + CCI + Volume Delta) is a custom-built trend-following and volume-based indicator that helps traders confirm market direction with high accuracy. It combines the MACD (Moving Average Convergence Divergence), CCI (Commodity Channel Index), and Volume Delta, ensuring that all three indicators align before making a trading decision. The goal is to filter out false signals and provide high-probability trade setups.
History & Development
Shavarie's MCV Indicator was developed by Shavarie Gordon, an experienced swing trader, to improve trend confirmation on Gold (XAUUSD) and other markets. After testing various indicators, Shavarie discovered that MACD, CCI, and Volume Delta together provide the best combination of trend strength, momentum, and real-time volume flow. This indicator was designed to eliminate lagging signals, improve win rates, and enhance market timing for both swing and scalping strategies.
How It Works & Calculations
MACD (Moving Average Convergence Divergence)
Measures momentum and trend strength using the difference between a 12-period EMA and a 26-period EMA.
The MACD line and Signal line crossover confirms buy/sell signals.
A rising MACD histogram confirms bullish strength, while a falling histogram confirms bearish strength.
CCI (Commodity Channel Index)
Measures how far the price is from its statistical average.
Above +100 → Overbought (strong trend continuation or reversal).
Below -100 → Oversold (strong trend continuation or reversal).
When CCI aligns with MACD, it confirms momentum strength.
Volume Delta
Measures the difference between buying and selling volume in real time.
A positive delta means more aggressive buying (bullish).
A negative delta means more aggressive selling (bearish).
Helps confirm MACD and CCI trends by showing real volume strength.
Key Takeaways & Features
✅ No false signals: All three indicators must align before entering a trade.
✅ Trend confirmation: Ensures momentum and volume agree before trading.
✅ Works on multiple timeframes: Designed for swing trading on the daily and scalping on 45 min + 5 min.
✅ Great for Gold & Metals: Optimized for XAUUSD, XAUJPY, XAU/AUD, and possibly Palladium (XPDUSD).
✅ Custom-built by a professional trader: Developed by Shavarie Gordon after extensive testing.
Summary
Shavarie’s MCV Indicator is a powerful and reliable trading tool that combines momentum, trend, and volume analysis. By ensuring that MACD, CCI, and Volume Delta align, it eliminates false signals and increases trade accuracy. Whether used for swing trading or scalping, this indicator helps traders enter high-probability trades with confidence.
G-VIDYA | QuantEdgeBIntroducing G-VIDYA by QuantEdgeB
____
🔹 Overview
The G-VIDYA | QuantEdgeB is a dynamic trend-following indicator that enhances market trend detection using Gaussian smoothing and an adaptive Variable Index Dynamic Average (VIDYA). It is designed to reduce noise, improve responsiveness, and adapt to volatility, making it a powerful tool for traders looking to capture long-term trends efficiently.
By integrating ATR-based filtering, the indicator creates a dynamic support and resistance band around VIDYA, allowing for more accurate trend confirmations. Additionally, traders have the option to enable trade labels for clearer visual signals.
This indicator is well-suited for medium to long-term trend traders, combining mathematical precision with market adaptability for robust trading strategies.
_____
🚀 Key Features
1. Gaussian Smoothing → Reduces market noise and smoothens price action.
2. VIDYA Adaptive Calculation → Adjusts dynamically based on market volatility.
3. ATR-Based Filtering → Creates a volatility-driven range around VIDYA.
4. Dynamic Trend Confirmation → Identifies bullish and bearish momentum shifts.
5. Trade Labels (Optional) → Can display Long/Cash labels on chart for better clarity.
6. Customizable Color Modes → Offers multiple visual themes for personalized experience.
7. Automated Alerts → Sends buy/sell alerts for crossover trend changes.
_____
📊 How It Works
1. Gaussian Smoothing is applied to the closing price to remove noise and improve signal clarity.
2. VIDYA Calculation dynamically adjusts to price movements, making it more reactive during high-volatility periods and stable in low-volatility environments.
3. ATR-Based Filtering establishes a dynamic range (Upper & Lower ATR Bands) around VIDYA:
- If price breaks above the upper ATR band, it signals a potential long trend.
- If price breaks below the lower ATR band, it signals a potential short trend.
4. The indicator assigns color-coded candles based on trend direction:
- Bullish Trend → Blue/Green (Uptrend)
- Bearish Trend → Red/Maroon (Downtrend)
5. Labels & Alerts (Optional)
- Users can activate Long/Cash labels to mark buy/sell opportunities.
- Built-in alerts trigger automatic notifications when trend direction changes.
_____
🎨 Visual Representation
- VIDYA Line → A smooth, trend-following line that dynamically adjusts to market conditions.
- Upper & Lower ATR Bands → Establishes a volatility-based corridor around VIDYA.
- Bar Coloring → Candles change color according to the detected trend.
- Long/Short Labels (Optional) → Displays trade entry/exit signals (can be enabled/disabled).
- Alerts → Generates trade notifications based on trend reversals.
______
⚙️ Default Settings
- Gaussian Smoothing
- Length: 4
- Sigma: 2.0
- VIDYA Settings
- VIDYA Length: 46
- Standard Deviation Length: 28
- ATR Settings
- ATR Length: 14
- ATR Multiplier: 1.3
____
💡 Who Should Use It?
✅ Trend Traders → Those who rely on medium-to-long-term trends for trading decisions.
✅ Swing Traders → Ideal for traders who want to capture trend reversals and ride momentum.
✅ Quantitative Analysts → Provides statistically driven smoothing and adaptive trend detection.
✅ Risk-Averse Traders → ATR filtering helps manage market volatility effectively.
_____
Conclusion
The G-VIDYA | QuantEdgeB is an advanced trend-following indicator that combines Gaussian smoothing, adaptive VIDYA filtering, and ATR-based dynamic trend analysis to deliver robust and reliable trade signals.
✅ Key Takeaways
📌 Adaptive & Dynamic: Adjusts to market conditions, making it effective for trend-following strategies.
📌 Noise Reduction: Gaussian smoothing helps filter out short-term fluctuations, improving signal clarity.
📌 Volatility Awareness: ATR-based filtering ensures better handling of market swings and trend reversals.
By blending mathematical precision and quantitative market analysis, G-VIDYA | QuantEdgeB offers a powerful edge in trend trading strategies.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
highs&lowsone of my first strategy: highs&lows
This strategy takes the highest high and the lowest low of a specified timeframe and specified bar count.
It will then takes the average between these two extremes to create a center line.
This creates a range of high middle and low.
Then the strategy takes the current market movement
which is the direct average(no specified timeframe and specified bar count) of the current high and low.
Using this "current market movement" within the range of high middle and low it determins when to buy and then sell the asset.
*********note***************
-this strategy is (bullish)
-works good with most futures assets that have volatility/ decent movement
(might add more details if I forget any)
(work in progress)
Fibonacci Cycle Finder🟩 Fibonacci Cycle Finder is an indicator designed to explore Fibonacci-based waves and cycles through visualization and experimentation, introducing a trigonometric approach to market structure analysis. Unlike traditional Fibonacci tools that rely on static horizontal levels, this indicator incorporates the dynamic nature of market cycles, using adjustable wavelength, phase, and amplitude settings to visualize the rhythm of price movements. By applying a sine function, it provides a structured way to examine Fibonacci relationships in a non-linear context.
Fibonacci Cycle Finder unifies Fibonacci principles with a wave-based method by employing adjustable parameters to align each wave with real-time price action. By default, the wave begins with minimal curvature, preserving the structural familiarity of horizontal Fibonacci retracements. By adjusting the input parameters, the wave can subtly transition from a horizontal line to a more pronounced cycle,visualizing cyclical structures within price movement. This projective structure extends potential cyclical outlines on the chart, opening deeper exploration of how Fibonacci relationships may emerge over time.
Fibonacci Cycle Finder further underscores a non-linear representation of price by illustrating how wave-based logic can uncover shifts that are missed by static retracement tools. Rather than imposing immediate oscillatory behavior, the indicator encourages a progressive approach, where the parameters may be incrementally modified to align wave structures with observed price action. This refinement process deepens the exploration of Fibonacci relationships, offering a systematic way to experiment with non-linear price dynamics. In doing so, it revisits fundamental Fibonacci concepts, demonstrating their broader adaptability beyond fixed horizontal retracements.
🌀 THEORY & CONCEPT 🌀
What if Fibonacci relationships could be visualized as dynamic waves rather than confined to fixed horizontal levels? Fibonacci Cycle Finder introduces a trigonometric approach to market structure analysis, offering a different perspective on Fibonacci-based cycles. This tool provides a way to visualize market fluctuations through cyclical wave motion, opening the door to further exploration of Fibonacci’s role in non-linear price behavior.
Traditional Fibonacci tools, such as retracements and extensions, have long been used to identify potential support and resistance levels. While valuable for analyzing price trends, these tools assume linear price movement and rely on static horizontal levels. However, market fluctuations often exhibit cyclical tendencies , where price follows natural wave-like structures rather than strictly adhering to fixed retracement points. Although Fibonacci-based tools such as arcs, fans, and time zones attempt to address these patterns, they primarily apply geometric projections. The Fibonacci Cycle Finder takes a different approach by mapping Fibonacci ratios along structured wave cycles, aligning these relationships with the natural curvature of market movement rather than forcing them onto rigid price levels.
Rather than replacing traditional Fibonacci methods, the Fibonacci Cycle Finder supplements existing Fibonacci theory by introducing an exploratory approach to price structure analysis. It encourages traders to experiment with how Fibonacci ratios interact with cyclical price structures, offering an additional layer of insight beyond static retracements and extensions. This approach allows Fibonacci levels to be examined beyond their traditional static form, providing deeper insights into market fluctuations.
📊 FIBONACCI WAVE IMPLEMENTATION 📊
The Fibonacci Cycle Finder uses two user-defined swing points, A and B, as the foundation for projecting these Fibonacci waves. It first establishes standard horizontal levels that correspond to traditional Fibonacci retracements, ensuring a baseline reference before wave adjustments are applied. By default, the wave is intentionally subtle— Wavelength is set to 1 , Amplitude is set to 1 , and Phase is set to 0 . In other words, the wave starts as “stretched out.” This allows a slow, measured start, encouraging users to refine parameters incrementally rather than producing abrupt oscillations. As these parameters are increased, the wave takes on more distinct sine and cosine characteristics, offering a flexible approach to exploring Fibonacci-based cyclicity within price action.
Three parameters control the shape of the Fibonacci wave:
1️⃣ Wavelength Controls the horizontal spacing of the wave along the time axis, determining the length of one full cycle from peak to peak (or trough to trough). In this indicator, Wavelength acts as a scaling input that adjusts how far the wave extends across time, rather than a strict mathematical “wavelength.” Lower values further stretch the wave, increasing the spacing between oscillations, while higher values compress it into a more frequent cycle. Each full cycle is divided into four quarter-cycle segments, a deliberate design choice to minimize curvature by default. This allows for subtle oscillations and smoother transitions, preventing excessive distortion while maintaining flexibility in wave projections. The wavelength is calculated relative to the A-B swing, ensuring that its scale adapts dynamically to the selected price range.
2️⃣ Amplitude Defines the vertical displacement of the wave relative to the baseline Fibonacci level. Higher values increase the height of oscillations, while lower values reduce the height, Negative values will invert the wave’s initial direction. The amplitude is dynamically applied in relation to the A-B swing direction, ensuring that an upward swing results in upward oscillations and a downward swing results in downward oscillations.
3️⃣ Phase Shifts the wave’s starting position along its cycle, adjusting alignment relative to the swing points. A phase of 0 aligns with a sine wave, where the cycle starts at zero and rises. A phase of 25 aligns with a cosine wave, starting at a peak and descending. A phase of 50 inverts the sine wave, beginning at zero but falling first, while a phase of 75 aligns with an inverted cosine , starting at a trough and rising. Intermediate values between these phases create gradual shifts in wave positioning, allowing for finer alignment with observed market structures.
By fine-tuning these parameters, users can adapt Fibonacci waves to better reflect observed market behaviors. The wave structure integrates with price movements rather than simply overlaying static levels, allowing for a more dynamic representation of cyclical price tendencies. This indicator serves as an exploratory tool for understanding potential market rhythms, encouraging traders to test and visualize how Fibonacci principles extend beyond their traditional applications.
🖼️ CHART EXAMPLES 🖼️
Following this downtrend, price interacts with curved Fibonacci levels, highlighting resistance at the 0.236 and 0.382 levels, where price stalls before pulling back. Support emerges at the 0.5, 0.618, and 0.786 levels, where price finds stability and rebounds
In this Fibonacci retracement, price initially finds support at the 1.0 level, following the natural curvature of the cycle. Resistance forms at 0.786, leading to a pullback before price breaks through and tests 0.618 as resistance. Once 0.618 is breached, price moves upward to test 0.5, illustrating how Fibonacci-based cycles may align with evolving market structure beyond static, horizontal retracements.
Following this uptrend, price retraces downward and interacts with the Fibonacci levels, demonstrating both support and resistance at key levels such as 0.236, 0.382, 0.5, and 0.618.
With only the 0.5 and 1.0 levels enabled, this chart remains uncluttered while still highlighting key price interactions. The short cycle length results in a mild curvature, aligning smoothly with market movement. Price finds resistance at the 0.5 level while showing strong support at 1.0, which follows the natural flow of the market. Keeping the focus on fewer levels helps maintain clarity while still capturing how price reacts within the cycle.
🛠️ CONFIGURATION AND SETTINGS 🛠️
Wave Parameters
Wavelength : Stretches or compresses the wave along the time axis, determining the length of one full cycle. Higher values extend the wave across more bars, while lower values compress it into a shorter time frame.
Amplitude : Expands or contracts the wave along the price axis, determining the height of oscillations relative to Fibonacci levels. Higher values increase the vertical range, while negative values invert the wave’s initial direction.
Phase : Offsets the wave along the time axis, adjusting where the cycle begins. Higher values shift the starting position forward within the wave pattern.
Fibonacci Levels
Levels : Enable or disable specific Fibonacci levels (0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0) to focus on relevant price zones.
Color : Modify level colors for enhanced visual clarity.
Visibility
Trend Line/Color : Toggle and customize the trend line connecting swing points A and B.
Setup Lines : Show or hide lines linking Fibonacci levels to projected waves.
A/B Labels Visibility : Control the visibility of swing point labels.
Left/Right Labels : Manage the display of Fibonacci level labels on both sides of the chart.
Fill % : Adjust shading intensity between Fibonacci levels (0% = no fill, 100% = maximum fill).
A and B Points (Time/Price):
These user-defined anchor points serve as the basis for Fibonacci wave calculations and can be manually set. A and B points can also be adjusted directly on the chart, with automatic synchronization to the settings panel, allowing for seamless modifications without needing to manually input values.
⚠️ DISCLAIMER ⚠️
The Fibonacci Cycle Finder is a visual analysis tool designed to illustrate Fibonacci relationships and serve as a supplement to traditional Fibonacci tools. While the indicator employs mathematical and geometric principles, no guarantee is made that its calculations will align with other Fibonacci tools or proprietary methods. Like all technical and visual indicators, the Fibonacci levels generated by this tool may appear to visually align with key price zones in hindsight. However, these levels are not intended as standalone signals for trading decisions. This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis.
🧠 BEYOND THE CODE 🧠
Fibonacci Cycle Finder is the latest indicator in the Fibonacci Geometry Series. Building on the concepts of the Fibonacci Time-Price Zones and the Fibonacci 3-D indicators, this tool introduces a trigonometric approach to market structure analysis.
The Fibonacci Cycle Finder indicator, like other xxattaxx indicators , is designed to encourage both education and community engagement. Your feedback and insights are invaluable to refining and enhancing the Fibonacci Cycle Finder indicator. We look forward to the creative applications, observations, and discussions this tool inspires within the trading community.
