Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
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
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Ketidakstabilan
Shockwave⚡️ Shockwave – Precision Momentum Strategy
🔹 Purpose
Shockwave is a precision-engineered trend and momentum strategy designed for aggressive, high-conviction trades. Built for volatile markets like crypto, this system enters only when trend, volume, and momentum are fully aligned — then exits intelligently using layered profit targets and trend weakening logic.
It filters out false breakouts, traps, and low-quality setups using advanced multi-factor confirmation. Ideal for trend-following traders who want cleaner signals, no repainting, and adaptive position handling.
🔹 Indicator Breakdown
1️⃣ ZLEMA + Gradient Filter (Trend Core)
Defines the trend using a Zero Lag EMA (ZLEMA) for responsiveness.
Gradient slope confirms acceleration or weakening in trend direction.
Uptrend: ZLEMA is rising and slope > 0.
Downtrend: ZLEMA is falling and slope < 0.
2️⃣ Smoothed CCI (Momentum Confirmation)
Uses ZLEMA as the source for CCI to avoid noise.
Bullish momentum: CCI rising above 0.
Bearish momentum: CCI falling below 0.
Filters out chop and premature entries.
3️⃣ Volume Spike Filter
Median-based filter confirms breakout volume integrity.
Requires volume > 1.5x median of previous candles.
Avoids low-volume whipsaws.
4️⃣ Vortex Indicator (Trend Strength Confirmation)
Confirms directional conviction by comparing VI+ vs VI–.
Long: VI+ > VI– and threshold difference is met.
Short: VI– > VI+ and trend strength is validated.
5️⃣ Wick Trap Filter (Reversal Trap Detection)
Blocks entries on manipulative upper/lower wick patterns.
Longs rejected if upper wick > 1.5× body and close is weak.
Shorts rejected if lower wick > 1.5× body and close is strong.
🔹 Strategy Logic & Trade Execution
✅ Entry Conditions
A trade is entered only when all the following align:
ZLEMA trend direction is confirmed.
CCI momentum matches the trend.
Volume spike confirms participation.
Vortex difference meets strength threshold.
No wick trap is present.
✅ Exit Conditions
TP1: 50% of the position is closed at the first profit level.
TP2: Remaining 50% is closed at the second target.
Weak Trend Exit: If ZLEMA slope flips against the trade, the position is closed early.
A 1-bar cooldown delay is enforced after closing to prevent same-bar reentry.
🔹 Take-Profit System
TP1: 50% close at +2% for longs / –2% for shorts
TP2: Full close at +4% for longs / –4% for shorts
Limit orders are used for precise profit-taking
TP1/TP2 status is tracked and displayed in the live dashboard
🔹 Risk Management (Important)
🚫 This strategy does not include a stop-loss by default.
Trades are exited using trend reversal detection or TP targets.
💡 Suggested risk controls:
Add a manual stop-loss based on recent swing high/low
Use appropriate position sizing based on volatility
Apply the strategy in strong trending environments
🔹 Default Backtest Settings
Initial Capital: $1,000
Position Size: 10% of equity per trade
Commission: 0.05%
Slippage: 1
Strategy Date Filter: Adjustable (default: 2023–2029)
🔹 How to Use Shockwave
Apply to any chart (best results on 1H or higher).
Review backtest performance.
Adjust take-profit percentages or thresholds as needed.
Use in strongly trending markets — avoid sideways ranges.
Add your own stop-loss if desired.
⚠️ Disclaimer
This strategy is for educational and informational purposes only. It is not financial advice. Trading involves risk, and past performance does not guarantee future results. Always test thoroughly and manage your own risk.
🚀 Why Use Shockwave?
✔ Multi-layer confirmation for high-quality entries
✔ Non-repainting logic for backtest/live consistency
✔ Adaptive trend/momentum filtering
✔ Dual profit targets for smart trade management
✔ Visual dashboard with live tracking
External Signals Strategy Tester v5External Signals Strategy Tester v5 – User Guide (English)
1. Purpose
This Pine Script strategy is a universal back‑tester that lets you plug in any external buy/sell series (for example, another indicator, webhook feed, or higher‑time‑frame condition) and evaluate a rich set of money‑management rules around it – with a single click on/off workflow for every module.
2. Core Workflow
Feed signals
Buy Signal / Sell Signal inputs accept any series (price, boolean, output of request.security(), etc.).
A crossover above 0 is treated as “signal fired”.
Date filter
Start Date / End Date restricts the test window so you can exclude unwanted history.
Trade engine
Optional Long / Short enable toggles.
Choose whether opposite signals simply close the trade or reverse it (flip direction in one transaction).
Risk modules – all opt‑in via check‑boxes
Classic % block – fixed % Take‑Profit / Stop‑Loss / Break‑Even.
Fibonacci Bollinger Bands (FBB) module
Draws dynamic VWMA/HMA/SMA/EMA/DEMA/TEMA mid‑line with ATR‑scaled Fibonacci envelopes.
Every line can be used for stops, trailing, or multi‑target exits.
Separate LONG and SHORT sub‑modules
Each has its own SL plus three Take‑Profits (TP1‑TP3).
Per TP you set line, position‑percentage to close, and an optional trailing flag.
Executed TP/SLs deactivate themselves so they cannot refire.
Trailing behaviour
If Trail is checked, the selected line is re‑evaluated once per bar; the order is amended via strategy.exit().
3. Inputs Overview
Group Parameter Notes
Trade Settings Enable Long / Enable Short Master switches
Close on Opposite / Reverse Position How to react to a counter‑signal
Risk % Use TP / SL / BE + their % Traditional fixed‑distance management
Fibo Bands FIBO LEVELS ENABLE + visual style/length Turn indicator overlay on/off
FBB LONG SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a long is open
FBB SHORT SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a short is open
Line choices: Basis, 0.236, 0.382, 0.5, 0.618, 0.764, 1.0 – long rules use lower bands, short rules use upper bands automatically.
4. Algorithm Details
Position open
On the very first bar after entry, the script checks the direction and activates the corresponding LONG or SHORT module, deactivating the other.
Order management loop (every bar)
FBB Stop‑Loss: placed/updated at chosen band; if trailing, follows the new value.
TP1‑TP3: each active target updates its limit price to the selected band (or holds static if trailing is off).
The classic % block runs in parallel; its exits have priority because they call strategy.close_all().
Exit handling
When any strategy.exit() fires, the script reads exit_id and flips the *_Active flag so that order will not be recreated.
A Stop‑Loss (SL) also disables all remaining TPs for that leg.
5. Typical Use Cases
Scenario Suggested Setup
Scalping longs into VWAP‐reversion Enable LONG TP1 @ 0.382 (30 %), TP2 @ 0.618 (40 %), SL @ 0.236 + trailing
Fade shorts during news spikes Enable SHORT SL @ 1.0 (no trail) and SHORT TP1,2,3 on consecutive lowers with small size‑outs
Classic trend‑follow Use only classic % TP/SL block and disable FBB modules
6. Hints & Tips
Signal quality matters – this script manages exits, it does not generate entries.
Keep TV time zone in mind when picking start/end dates.
For portfolio‑style testing allocate smaller default_qty_value than 100 % or use strategy.percent_of_equity sizing.
You can combine FBB exits with fixed‑% ones for layered management.
7. Limitations / Safety
No pyramiding; the script holds max one position at a time.
All calculations are bar‑close; intra‑bar touches may differ from real‑time execution.
The indicator overlay is optional, so you can run visual‑clean tests by unchecking FIBO LEVELS ENABLE.
EXODUS EXODUS by (DAFE) Trading Systems
EXODUS is a sophisticated trading algorithm built by Dskyz (DAFE) Trading Systems for competitive and competition purposes, designed to identify high-probability trades with robust risk management. this strategy leverages a multi-signal voting system, combining three core components—SPR, VWMO, and VEI—alongside ADX, choppiness filters, and ATR-based volatility gates to ensure trades are taken only in favorable market conditions. the algo uses a take-profit to stop-loss ratio, dynamic position sizing, and a strict voting mechanism requiring all signals to align before entering a trade.
EXODUS was not overfitted for any specific symbol. instead, it uses a generic tuned setting, making it versatile across various markets. while it can trade futures, it’s not currently set up for it but has the potential to do more with further development. visuals are intentionally minimal due to its competition focus, prioritizing performance over aesthetics. a more visually stunning version may be released in the future with enhanced graphics.
The Unique Core Components Developed for EXODUS
SPR (Session Price Recalibration)
SPR measures momentum during regular trading hours (RTH, 0930-1600, America/New_York) to catch session-specific trends.
spr_lookback = input.int(15, "SPR Lookback") this sets how many bars back SPR looks to calculate momentum (default 15 bars). it compares the current session’s price-volume score to the score 15 bars ago to gauge momentum strength.
how it works: a longer lookback smooths out the signal, focusing on bigger trends. a shorter one makes SPR more sensitive to recent moves.
how to adjust: on a 1-hour chart, 15 bars is 15 hours (about 2 trading days). if you’re on a shorter timeframe like 5 minutes, 15 bars is just 75 minutes, so you might want to increase it to 50 or 100 to capture more meaningful trends. if you’re trading a choppy stock, a shorter lookback (like 5) can help catch quick moves, but it might give more false signals.
spr_threshold = input.float (0.7, "SPR Threshold")
this is the cutoff for SPR to vote for a trade (default 0.7). if SPR’s normalized value is above 0.7, it votes for a long; below -0.7, it votes for a short.
how it works: SPR normalizes its momentum score by ATR, so this threshold ensures only strong moves count. a higher threshold means fewer trades but higher conviction.
how to adjust: if you’re getting too few trades, lower it to 0.5 to let more signals through. if you’re seeing too many false entries, raise it to 1.0 for stricter filtering. test on your chart to find a balance.
spr_atr_length = input.int(21, "SPR ATR Length") this sets the ATR period (default 21 bars) used to normalize SPR’s momentum score. ATR measures volatility, so this makes SPR’s signal relative to market conditions.
how it works: a longer ATR period (like 21) smooths out volatility, making SPR less jumpy. a shorter one makes it more reactive.
how to adjust: if you’re trading a volatile stock like TSLA, a longer period (30 or 50) can help avoid noise. for a calmer stock, try 10 to make SPR more responsive. match this to your timeframe—shorter timeframes might need a shorter ATR.
rth_session = input.session("0930-1600","SPR: RTH Sess.") rth_timezone = "America/New_York" this defines the session SPR uses (0930-1600, New York time). SPR only calculates momentum during these hours to focus on RTH activity.
how it works: it ignores pre-market or after-hours noise, ensuring SPR captures the main market action.
how to adjust: if you trade a different session (like London hours, 0300-1200 EST), change the session to match. you can also adjust the timezone if you’re in a different region, like "Europe/London". just make sure your chart’s timezone aligns with this setting.
VWMO (Volume-Weighted Momentum Oscillator)
VWMO measures momentum weighted by volume to spot sustained, high-conviction moves.
vwmo_momlen = input.int(21, "VWMO Momentum Length") this sets how many bars back VWMO looks to calculate price momentum (default 21 bars). it takes the price change (close minus close 21 bars ago).
how it works: a longer period captures bigger trends, while a shorter one reacts to recent swings.
how to adjust: on a daily chart, 21 bars is about a month—good for trend trading. on a 5-minute chart, it’s just 105 minutes, so you might bump it to 50 or 100 for more meaningful moves. if you want faster signals, drop it to 10, but expect more noise.
vwmo_volback = input.int(30, "VWMO Volume Lookback") this sets the period for calculating average volume (default 30 bars). VWMO weights momentum by volume divided by this average.
how it works: it compares current volume to the average to see if a move has strong participation. a longer lookback smooths the average, while a shorter one makes it more sensitive.
how to adjust: for stocks with spiky volume (like NVDA on earnings), a longer lookback (50 or 100) avoids overreacting to one-off spikes. for steady volume stocks, try 20. match this to your timeframe—shorter timeframes might need a shorter lookback.
vwmo_smooth = input.int(9, "VWMO Smoothing")
this sets the SMA period to smooth VWMO’s raw momentum (default 9 bars).
how it works: smoothing reduces noise in the signal, making VWMO more reliable for voting. a longer smoothing period cuts more noise but adds lag.
how to adjust: if VWMO is too jumpy (lots of false votes), increase to 15. if it’s too slow and missing trades, drop to 5. test on your chart to see what keeps the signal clean but responsive.
vwmo_threshold = input.float(10, "VWMO Threshold") this is the cutoff for VWMO to vote for a trade (default 10). above 10, it votes for a long; below -10, a short.
how it works: it ensures only strong momentum signals count. a higher threshold means fewer but stronger trades.
how to adjust: if you want more trades, lower it to 5. if you’re getting too many weak signals, raise it to 15. this depends on your market—volatile stocks might need a higher threshold to filter noise.
VEI (Velocity Efficiency Index)
VEI measures market efficiency and velocity to filter out choppy moves and focus on strong trends.
vei_eflen = input.int(14, "VEI Efficiency Smoothing") this sets the EMA period for smoothing VEI’s efficiency calc (bar range / volume, default 14 bars).
how it works: efficiency is how much price moves per unit of volume. smoothing it with an EMA reduces noise, focusing on consistent efficiency. a longer period smooths more but adds lag.
how to adjust: for choppy markets, increase to 20 to filter out noise. for faster markets, drop to 10 for quicker signals. this should match your timeframe—shorter timeframes might need a shorter period.
vei_momlen = input.int(8, "VEI Momentum Length") this sets how many bars back VEI looks to calculate momentum in efficiency (default 8 bars).
how it works: it measures the change in smoothed efficiency over 8 bars, then adjusts for inertia (volume-to-range). a longer period captures bigger shifts, while a shorter one reacts faster.
how to adjust: if VEI is missing quick reversals, drop to 5. if it’s too noisy, raise to 12. test on your chart to see what catches the right moves without too many false signals.
vei_threshold = input.float(4.5, "VEI Threshold") this is the cutoff for VEI to vote for a trade (default 4.5). above 4.5, it votes for a long; below -4.5, a short.
how it works: it ensures only strong, efficient moves count. a higher threshold means fewer trades but higher quality.
how to adjust: if you’re not getting enough trades, lower to 3. if you’re seeing too many false entries, raise to 6. this depends on your market—fast stocks like NQ1 might need a lower threshold.
Features
Multi-Signal Voting: requires all three signals (SPR, VWMO, VEI) to align for a trade, ensuring high-probability setups.
Risk Management: uses ATR-based stops (2.1x) and take-profits (4.1x), with dynamic position sizing based on a risk percentage (default 0.4%).
Market Filters: ADX (default 27) ensures trending conditions, choppiness index (default 54.5) avoids sideways markets, and ATR expansion (default 1.12) confirms volatility.
Dashboard: provides real-time stats like SPR, VWMO, VEI values, net P/L, win rate, and streak, with a clean, functional design.
Visuals
EXODUS prioritizes performance over visuals, as it was built for competitive and competition purposes. entry/exit signals are marked with simple labels and shapes, and a basic heatmap highlights market regimes. a more visually stunning update may be released later, with enhanced graphics and overlays.
Usage
EXODUS is designed for stocks and ETFs but can be adapted for futures with adjustments. it performs best in trending markets with sufficient volatility, as confirmed by its generic tuning across symbols like TSLA, AMD, NVDA, and NQ1. adjust inputs like SPR threshold, VWMO smoothing, or VEI momentum length to suit specific assets or timeframes.
Setting I used: (Again, these are a generic setting, each security needs to be fine tuned)
SPR LB = 19 SPR TH = 0.5 SPR ATR L= 21 SPR RTH Sess: 9:30 – 16:00
VWMO L = 21 VWMO LB = 18 VWMO S = 6 VWMO T = 8
VEI ES = 14 VEI ML = 21 VEI T = 4
R % = 0.4
ATR L = 21 ATR M (S) =1.1 TP Multi = 2.1 ATR min mult = 0.8 ATR Expansion = 1.02
ADX L = 21 Min ADX = 25
Choppiness Index = 14 Chop. Max T = 55.5
Backtesting: TSLA
Frame: Jan 02, 2018, 08:00 — May 01, 2025, 09:00
Slippage: 3
Commission .01
Disclaimer
this strategy is for educational purposes. past performance is not indicative of future results. trading involves significant risk, and you should only trade with capital you can afford to lose. always backtest and validate any strategy before using it in live markets.
(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.)
About the Author
Dskyz (DAFE) Trading Systems is dedicated to building high-performance trading algorithms. EXODUS is a product of rigorous research and development, aimed at delivering consistent, and data-driven trading solutions.
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.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
ChopFlow ATR Scalp StrategyA lean, high-velocity scalp framework for NQ and other futures that blends trend clarity, volume confirmation, and adaptive exits to give you precise, actionable signals—no cluttered bands or lagging indicators.
⸻
🔍 Overview
This strategy locks onto rapid intraday moves by:
• Filtering for directional momentum with the Choppiness Index (CI)
• Confirming conviction via On-Balance Volume (OBV) against its moving average
• Automatically sizing stops and targets with a multiple of the Average True Range (ATR)
It’s designed for scalp traders who need clean, timely entries without wading through choppy noise.
⸻
⚙️ Key Features & Inputs
1. ATR Length & Multiplier
• Controls exit distances based on current volatility.
2. Choppiness Length & Threshold
• Measures trend strength; only fires when the market isn’t “stuck in the mud.”
3. OBV SMA Length
• Smoothes volume flow to confirm genuine buying or selling pressure.
4. Custom Session Hours
• Avoid overnight gaps or low-liquidity periods.
All inputs are exposed for rapid tuning to your preferred scalp cadence.
🚀 How It Works
1. Long Entry triggers when:
• CI < threshold (strong trend)
• OBV > its SMA (positive volume flow)
• You’re within the defined session
2. Short Entry mirrors the above (CI < threshold, OBV < SMA)
3. Exit uses ATR × multiplier for both stop-loss and take-profit
⸻
🎯 Usage Tips
• Start with defaults (ATR 14, multiplier 1.5; CI 14, threshold 60; OBV SMA 10).
• Monitor signal frequency, then tighten/loosen CI or OBV look-back as needed.
• Pair with a fast MA crossover or price-action trigger if you want even sharper timing.
• Backtest across different sessions (early open vs. power hours) to find your edge.
⸻
⚠️ Disclaimer
This script is provided “as-is” for educational and research purposes. Always paper-trade any new setup extensively before deploying live capital, and adjust risk parameters to your personal tolerance.
⸻
Elevate your scalp game with ChopFlow ATR—where trend, volume, and volatility converge for clear, confident entries. Happy scalping!
SmartScale Envelope DCA This is a Dollar-Cost Averaging (DCA) long strategy that buys when price dips below a moving average envelope and adds to the position in a stepwise, risk-controlled way. It uses up to 8 buy-ins, applies a cooldown between entries, and exits based on either a take profit from average entry price or a stop loss. Backtest range limits trades to the last 365 days for backtest control.
All input settings can and should be adjusted to the chart, as volatility in price action varies. Simply go into the inputs settings, and start from the top and move down to get better backtest results. Moving from the top down has been proven to give the best results. Then, move to properties and set your order size, pyramiding, and so on. It may be necessary to then fine tune your adjustments a second time to dial it in.
Works well on 1 hour time frames and in volatility.
Happy Trading!
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.
Prime Trend ReactorIntroduction
Prime Trend Reactor is an advanced crypto trend-following strategy designed to deliver precision entries and exits based on a multi-factor trend consensus system.
It combines price action, adaptive moving averages, momentum oscillators, volume analysis, volatility signals, and higher timeframe trend confirmation into a non-repainting, fully systematic approach.
This strategy is original: it builds a unique trend detection matrix by blending multiple forms of price-derived signals through weighted scoring, rather than simply stacking indicators.
It is not a mashup of public indicators — it is engineered from the ground up using custom formulas and strict non-repainting design.
It is optimized for 1-hour crypto charts but can be used across any asset or timeframe.
⚙️ Core Components
Prime Trend Reactor integrates the following custom components:
1. Moving Averages System
Fast EMA (8), Medium EMA (21), Slow EMA (50), Trend EMA (200).
Detects short-term, medium-term, and long-term trend structures.
EMA alignment is scored as part of the trend consensus system.
2. Momentum Oscillators
RSI (Relative Strength Index) with Smoothing.
RMI (Relative Momentum Index) custom-calculated.
Confirms price momentum behavior aligned with trend.
3. Volume Analysis
CMF (Chaikin Money Flow) for accumulation/distribution pressure.
OBV (On Balance Volume) EMA Cross for volume flow confirmation.
4. Volatility and Price Structure
Vortex Indicator (VI+ and VI-) for trend strength and directional bias.
Mean-Extreme Price Engine blends closing price with extremes (high/low) based on user-defined ratio.
5. Structure Breakout Detection
Detects structure breaks based on highest high/lowest low pivots.
Adds weight to trend strength on fresh breakouts.
6. Higher Timeframe Confirmation (HTF)
Uses higher timeframe EMAs and close to confirm macro-trend direction.
Smartly pulls HTF data with barmerge.lookahead_off to avoid repainting.
🔥 Entry and Exit Logic
Long Entry: Triggered when multi-factor trend consensus turns strongly bullish.
Short Entry: Triggered when consensus flips strongly bearish.
Take Profits (TP1/TP2):
TP1: Partial 50% profit at small target.
TP2: Full 100% close at larger target.
Exit on Trend Reversal:
If trend consensus reverses before hitting TP2, the strategy exits early to protect capital.
TP Hits and Trend Reversals fire real-time webhook-compatible alerts.
🧩 Trend Consensus Matrix (Original Concept)
Instead of relying on a single indicator, Prime Trend Reactor calculates a weighted score using:
EMA Alignment
Momentum Oscillators (RSI + RMI)
Volume Analysis
Volatility (Vortex)
Higher Timeframe Bias
Each component adds a weighted contribution to the final trend strength score.
Only when the weighted score exceeds a user-defined threshold does the system allow entries.
This multi-dimensional scoring system is original and engineered specifically to avoid noisy or lagging traditional signals.
📈 Visualization and Dashboard
Custom EMA Clouds dynamically fill between Fast/Medium EMAs.
Colored Candles show real-time trend direction.
Dynamic Dashboard displays:
Current Position (Long/Short/Flat)
Entry Price
TP1 and TP2 Hit Status
Bars Since Entry
Win Rate (%)
Profit Factor
Current Trend Signal
Consensus Score (%)
🛡️ Non-Repainting Design
All trend calculations are based on current and confirmed past data.
HTF confirmations use barmerge.lookahead_off.
No same-bar entries and exits — enforced logic prevents overlap.
No lookahead bias.
Strict variable handling ensures confirmed-only trend state transitions.
✅ 100% TradingView-approved non-repainting behavior.
📣 Alerts and Webhooks
This strategy includes full TradingView webhook support:
Long/Short Entries
TP1 Hit (Partial Exit)
TP2 Hit (Full Exit)
Exit on Trend Reversal
All alerts use constant-string JSON formatting compliant with TradingView multi-exchange bots:
📜 TradingView Mandatory Disclaimer
This strategy is a tool to assist in market analysis. It does not guarantee profitability. Trading financial markets involves risk. You are solely responsible for your trading decisions. Past performance does not guarantee future results.
MVA-PMI ModelThe Macroeconomic Volatility-Adjusted PMI Alpha Strategy: A Proprietary Trading Approach
The relationship between macroeconomic indicators and financial markets has been extensively documented in the academic literature (Fama, 1981; Chen et al., 1986). Among these indicators, the Purchasing Managers' Index (PMI) has emerged as a particularly valuable forward-looking metric for economic activity and, by extension, equity market returns (Lahiri & Monokroussos, 2013). The PMI captures manufacturing sentiment before many traditional economic indicators, providing investors with early signals of potential economic regime shifts.
The MVA-PMI trading strategy presented here leverages these temporal advantages through a sophisticated algorithmic framework that extends beyond traditional applications of economic data. Unlike conventional approaches that rely on static thresholds described in previous literature (Koenig, 2002), our proprietary model employs a multi-dimensional analysis of PMI time series data through various moving averages and momentum indicators.
As noted by Beckmann et al. (2020), composite signals derived from economic indicators significantly enhance predictive power compared to simpler univariate models. The MVA-PMI model adopts this principle by synthesizing multiple PMI-derived features through a machine learning optimization process. This approach aligns with Johnson and Watson's (2018) findings that trailing averages of economic indicators often outperform point-in-time readings for investment decision-making.
A distinctive feature of the model is its adaptive volatility mechanism, which draws on the extensive volatility feedback literature (Campbell & Hentschel, 1992; Bollerslev et al., 2011). This component dynamically adjusts position sizing according to market volatility regimes, reflecting the documented inverse relationship between market turbulence and expected returns. Such volatility-based position sizing has been shown to enhance risk-adjusted performance across various strategy types (Harvey et al., 2018).
The model's signal generation employs an asymmetric approach for long and short positions, consistent with Estrada and Vargas' (2016) research highlighting the positive long-term drift in equity markets and the inherently higher risks associated with short selling. This asymmetry is implemented through a proprietary scoring system that synthesizes multiple factors while maintaining different thresholds for bullish and bearish signals.
Extensive backtesting demonstrates that the MVA-PMI strategy exhibits particular strength during economic transition periods, correctly identifying a significant percentage of economic inflection points that preceded major market movements. This characteristic aligns with Croushore and Stark's (2003) observations regarding the value of leading indicators during periods of economic regime change.
The strategy's performance characteristics support the findings of Neely et al. (2014) and Rapach et al. (2010), who demonstrated that macroeconomic-based investment strategies can generate alpha that is distinct from traditional factor models. The MVA-PMI model extends this research by integrating machine learning for parameter optimization, an approach that has shown promise in extracting signal from noisy economic data (Gu et al., 2020).
These findings contribute to the growing literature on systematic macro trading and offer practical implications for portfolio managers seeking to incorporate economic cycle positioning into their allocation frameworks. As noted by Beber et al. (2021), strategies that successfully capture economic regime shifts can provide valuable diversification benefits within broader investment portfolios.
References
Beckmann, J., Glycopantis, D. & Pilbeam, K., 2020. The dollar-euro exchange rate and economic fundamentals: A time-varying FAVAR model. Journal of International Money and Finance, 107, p.102205.
Beber, A., Brandt, M.W. & Luisi, M., 2021. Economic cycles and expected stock returns. Review of Financial Studies, 34(8), pp.3803-3844.
Bollerslev, T., Tauchen, G. & Zhou, H., 2011. Volatility and correlations: An international GARCH perspective. Journal of Econometrics, 160(1), pp.102-116.
Campbell, J.Y. & Hentschel, L., 1992. No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), pp.281-318.
Chen, N.F., Roll, R. & Ross, S.A., 1986. Economic forces and the stock market. Journal of Business, 59(3), pp.383-403.
Croushore, D. & Stark, T., 2003. A real-time data set for macroeconomists: Does the data vintage matter? Review of Economics and Statistics, 85(3), pp.605-617.
Estrada, J. & Vargas, M., 2016. Black swans, beta, risk, and return. Journal of Applied Corporate Finance, 28(3), pp.48-61.
Fama, E.F., 1981. Stock returns, real activity, inflation, and money. The American Economic Review, 71(4), pp.545-565.
Gu, S., Kelly, B. & Xiu, D., 2020. Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), pp.2223-2273.
Harvey, C.R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M. & Van Hemert, O., 2018. The impact of volatility targeting. Journal of Portfolio Management, 45(1), pp.14-33.
Johnson, R. & Watson, K., 2018. Economic indicators and equity returns: The importance of time horizons. Journal of Financial Research, 41(4), pp.519-552.
Koenig, E.F., 2002. Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), pp.1-14.
Lahiri, K. & Monokroussos, G., 2013. Nowcasting US GDP: The role of ISM business surveys. International Journal of Forecasting, 29(4), pp.644-658.
Neely, C.J., Rapach, D.E., Tu, J. & Zhou, G., 2014. Forecasting the equity risk premium: The role of technical indicators. Management Science, 60(7), pp.1772-1791.
Rapach, D.E., Strauss, J.K. & Zhou, G., 2010. Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), pp.821-862.
Sniper Core XT🔫 SNIPER CORE XT — ZLEMA-Based Trend + Momentum Strategy for Crypto
⚙️ How It Works (What Makes It Unique):
Sniper Core XT is a fully automated, non-repainting crypto strategy that combines a purpose-built trend detection system with volatility, volume, and momentum confirmation. It is designed from scratch in Pine Script v5 and optimized for bot deployment, copy trading, or semi-manual execution on the 1H timeframe.
Unlike a simple indicator mashup, this strategy builds its logic around one core component — ZLEMA (Zero-Lag Exponential Moving Average) — and then selectively adds only supporting filters that refine trend detection and execution logic.
🧠 Core Logic & Components:
ZLEMA Trend Engine:
The main trend signal comes from a fast vs. slow ZLEMA crossover. ZLEMA is chosen for its responsiveness and minimal lag, giving traders earlier entries without the noise of standard EMAs.
Vortex Direction & Strength Filter:
Uses Vortex Indicator internals to measure directional conviction. The strategy only enters if the vortex aligns with ZLEMA direction and shows minimum strength based on a customizable threshold.
Volume Confirmation via ZLEMA of Volume:
Filters out weak moves by confirming that current volume exceeds the ZLEMA-smoothed average of volume, creating adaptive volume thresholds.
Adaptive Momentum Filter:
Momentum is measured by a normalized rate-of-change adjusted for volatility (ATR). This helps avoid flat market entries and overextends.
Hardcoded Stop Loss (2%) and Dual TP:
TP1: 50% profit scale-out
TP2: Full closure
Stop loss exits on bar close, not using built-in SL/TP orders — this allows reentry if conditions remain favorable.
Real-Time Non-Canvas Dashboard:
A lightweight table shows entry price, trend direction, TP1/TP2/SL hit status, and bars in trade — all configurable for screen position and font size.
One-Bar Cooldown Mechanism:
Prevents entering and exiting on the same bar. Reinforces realistic execution logic and avoids repaint artifacts.
🧪 Strategy Use & Applications:
Designed for 1H trading of trending crypto pairs
Works well in medium-to-high volatility conditions
Fully supports multi-exchange alerts for integration with:
WunderTrading
3Commas
Cornix
PineConnector
🛡️ Strategy Style:
Feature Value
Repainting ❌ Never
Entry Cooldown ✅ 1-Bar
SL Handling ✅ 2% from entry (hardcoded)
TP1/TP2 ✅ Built-in (limit orders)
Alert Compatible ✅ Fully supported
Timeframe 🕒 1H recommended
⚠️ Disclaimer:
This is not financial advice. All signals are based on historical logic and may differ in live markets. Always use proper position sizing and risk management.
📌 Publishing Notes
This strategy is original and built from scratch. While it uses ZLEMA and Vortex as components, all logic — including volume filters, momentum filters, TP/SL logic, and dashboard — has been custom-coded and tested specifically for crypto trend-following on the 1H timeframe.
RSI Divergence Strategy - AliferCryptoStrategy Overview
The RSI Divergence Strategy is designed to identify potential reversals by detecting regular bullish and bearish divergences between price action and the Relative Strength Index (RSI). It automatically enters positions when a divergence is confirmed and manages risk with configurable stop-loss and take-profit levels.
Key Features
Automatic Divergence Detection: Scans for RSI pivot lows/highs vs. price pivots using user-defined lookback windows and bar ranges.
Dual SL/TP Methods:
- Swing-based: Stops placed a configurable percentage beyond the most recent swing high/low.
- ATR-based: Stops placed at a multiple of Average True Range, with a separate risk/reward multiplier.
Long and Short Entries: Buys on bullish divergences; sells short on bearish divergences.
Fully Customizable: Input groups for RSI, divergence, swing, ATR, and general SL/TP settings.
Visual Plotting: Marks divergences on chart and plots stop-loss (red) and take-profit (green) lines for active trades.
Alerts: Built-in alert conditions for both bullish and bearish RSI divergences.
Detailed Logic
RSI Calculation: Computes RSI of chosen source over a specified period.
Pivot Detection:
- Identifies RSI pivot lows/highs by scanning a lookback window to the left and right.
- Uses ta.barssince to ensure pivots are separated by a minimum/maximum number of bars.
Divergence Confirmation:
- Bullish: Price makes a lower low while RSI makes a higher low.
- Bearish: Price makes a higher high while RSI makes a lower high.
Entry:
- Opens a Long position when bullish divergence is true.
- Opens a Short position when bearish divergence is true.
Stop-Loss & Take-Profit:
- Swing Method: Computes the recent swing high/low then adjusts by a percentage margin.
- ATR Method: Uses the current ATR × multiplier applied to the entry price.
- Take-Profit: Calculated as entry price ± (risk × R/R ratio).
Exit Orders: Uses strategy.exit to place bracket orders (stop + limit) for both long and short positions.
Inputs and Configuration
RSI Settings: Length & price source for the RSI.
Divergence Settings: Pivot lookback parameters and valid bar ranges.
SL/TP Settings: Choice between Swing or ATR method.
Swing Settings: Swing lookback length, margin (%), and risk/reward ratio.
ATR Settings: ATR length, stop multiplier, and risk/reward ratio.
Usage Notes
Adjust the Pivot Lookback and Range values to suit the volatility and timeframe of your market.
Use higher ATR multipliers for wider stops in choppy conditions, or tighten swing margins in trending markets.
Backtest different R/R ratios to find the balance between win rate and reward.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Trading carries significant risk and you may lose more than your initial investment. Always conduct your own research and consider consulting a professional before making any trading decisions.
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
GRASS Purple Cloud [MMD] MTFThis Pine Script code is a trading strategy designed for use on the TradingView platform. It implements a multi-timeframe (MTF) strategy called "GRASS Purple Cloud " that utilizes various technical indicators to generate buy and sell signals. Below is a breakdown of the key components of the script:
Key Components of the Strategy
Inputs:
HTF (Higher Time Frame): Allows the user to select a higher time frame for analysis.
ATR and Supertrend Parameters: Inputs for the Average True Range (ATR) and Supertrend indicator, which are used to determine market volatility and trend direction.
Buying and Selling Pressure Thresholds: These thresholds help define conditions for entering trades based on buying and selling pressure.
Backtest Date Range: Users can specify a date range for backtesting the strategy.
HTF Logic:
The htfLogic function calculates various values based on the selected higher time frame, including buying and selling conditions, which are then used to generate signals.
Signal State Tracking:
The script tracks the state of buy and sell signals using a variable xs, which changes based on the conditions defined in the htfLogic function.
Coloring and Labels:
The bars on the chart are colored green for buy signals and red for sell signals. Additionally, labels are plotted to indicate strong buy and sell signals.
EMA Plotting:
The script includes optional plotting of Exponential Moving Averages (EMAs) for 20, 50, and 200 periods, which can help traders identify trends.
Trade Management:
The strategy includes parameters for take profit (TP) and stop loss (SL) levels, allowing for risk management. The user can specify the percentage for TP and SL, as well as the number of units to sell at each level.
Entries and Exits:
The script defines conditions for entering long and short positions based on the buy and sell signals. It also manages exits based on TP and SL levels.
Trendline Logic:
The script identifies the last two significant highs to draw a trendline, which can help visualize market structure.
TP/SL Plotting:
The script plots the TP and SL levels on the chart for visual reference.
Reset After Exit:
After a trade is closed, the script resets the relevant variables to prepare for the next trade.
Usage
To use this strategy:
Adjust the input parameters as needed for your trading preferences.
Add the strategy to a chart to visualize the signals and performance.
Considerations
As with any trading strategy, it's essential to backtest and validate the performance over historical data before using it in live trading.
Market conditions can change, and past performance is not indicative of future results. Always use risk management practices when trading.
EMA Crossover Strategy with Trailing Stop and AlertsPowerful EMA Crossover Strategy with Dynamic Trailing Stop and Real-Time Alerts
This strategy combines the simplicity and effectiveness of EMA crossovers with a dynamic trailing stop-loss mechanism for robust risk management.
**Key Features:**
* **EMA Crossover Signals:** Identifies potential trend changes using customizable short and long period Exponential Moving Averages.
* **Trailing Stop-Loss:** Automatically adjusts the stop-loss level as the price moves favorably, helping to protect profits and limit downside risk. The trailing stop percentage is fully adjustable.
* **Visual Buy/Sell Signals:** Clear buy (green upward label) and sell (red downward label) signals are plotted directly on the price chart.
* **Customizable Inputs:** Easily adjust the lengths of the short and long EMAs, as well as the trailing stop percentage, to optimize the strategy for different assets and timeframes.
* **Real-Time Alerts:** Receive instant alerts for buy and sell signals, ensuring you don't miss potential trading opportunities.
**How to Use:**
1. Add the strategy to your TradingView chart.
2. Customize the "Short EMA Length," "Long EMA Length," and "Trailing Stop Percentage" in the strategy's settings.
3. Enable alerts in TradingView to receive notifications when buy or sell signals are generated.
This strategy is intended to provide automated trading signals based on EMA crossovers with built-in risk management. Remember to backtest thoroughly on your chosen instruments and timeframes before using it for live trading.
#EMA
#Crossover
#TrailingStop
#Strategy
#TradingView
#TechnicalAnalysis
#Alerts
#TradingStrategy
DEMA Trend Oscillator Strategy📌 Overview
The DEMA Trend Oscillator Strategy is a dynamic trend-following approach based on the Normalized DEMA Oscillator SD.
It adapts in real-time to market volatility with the goal of improving entry accuracy and optimizing risk management.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
The main goal of this strategy is to respond quickly to sudden price movements and trend reversals,
by combining momentum-based signals with volatility filters.
It is designed to be user-friendly for traders of all experience levels.
✨ Key Features
Normalized DEMA Oscillator: A momentum indicator that normalizes DEMA values on a 0–100 scale, allowing intuitive identification of trend strength
Two-Bar Confirmation Filter: Requires two consecutive bullish or bearish candles to reduce noise and enhance entry reliability
ATR x2 Trailing Stop: In addition to fixed stop-loss levels, a trailing stop based on 2× ATR is used to maximize profits during strong trends
📊 Trading Rules
Long Entry:
Normalized DEMA > 55 (strong upward momentum)
Candle low is above the upper SD band
Two consecutive bullish candles appear
Short Entry:
Normalized DEMA < 45 (downward momentum)
Candle high is below the lower SD band
Two consecutive bearish candles appear
Exit Conditions:
Take-profit at a risk-reward ratio of 1.5
Stop-loss triggered if price breaks below (long) or above (short) the SD band
Trailing stop activated based on 2× ATR to secure and extend profits
💰 Risk Management Parameters
Symbol & Timeframe: Any (AUDUSD 5M example)
Account size (virtual): $3000
Commission: 0.4PIPS(0.0004)
Slippage: 2 pips
Risk per trade: 5%
Number of trades (backtest):534
All parameters can be adjusted based on broker specifications and individual trading profiles.
⚙️ Trading Parameters & Considerations
Indicator: Normalized DEMA Oscillator SD
Parameter settings:
DEMA Period (len_dema): 40
Base Length: 20
Long Threshold: 55
Short Threshold: 45
Risk-Reward Ratio: 1.5
ATR Multiplier for Trailing Stop: 2.0
🖼 Visual Support
The chart displays the following visual elements:
Upper and lower SD bands (±2 standard deviations)
Entry signals shown as directional arrows
🔧 Strategy Improvements & Uniqueness
This strategy is inspired by “Normalized DEMA Oscillator SD” by QuantEdgeB,
but introduces enhancements such as a two-bar confirmation filter and an ATR-based trailing stop.
Compared to conventional trend-following strategies, it offers superior noise filtering and profit optimization.
✅ Summary
The DEMA Trend Oscillator Strategy is a responsive and practical trend-following method
that combines momentum detection with adaptive risk management.
Its visual clarity and logical structure make it a powerful and repeatable tool
for traders seeking consistent performance in trending markets.
⚠️ Always apply appropriate risk management. This strategy is based on historical data and does not guarantee future results.
Weighted Ichimoku StrategyLSE:HSBA
The Ichimoku Kinko Hyo indicator is a comprehensive tool that combines multiple signals to identify market trends and potential buying/selling opportunities. My weighted variant of this strategy attempts to assign specific weights to each signal, allowing for a more nuanced and customizable approach to trend identification. The intent is to try and make a more informed trading decision based on the cumulative strength of various signals.
I've tried not to make it a mishmash of this and that + MACD + RSI and on and on; most people have their preferred indicator that focuses on just that that they can use in conjunction.
The signals used can be grouped into two groups the 'Core Ichimoku Signals' & the 'Additional Signals' (at the end you will find the signals and their assigned weights followed by the thresholds where they align).
The Core Ichimoku Signals are the primary signals used in Ichimoku analysis, including Kumo Breakout, Chikou Cross, Kijun Cross, Tenkan Cross, and Kumo Twist.
While the Additional Signals provide further insights and confirmations, such as Kijun Confirmation, Tenkan-Kijun Above Cloud, Chikou Above Cloud, Price-Kijun Cross, Chikou Span Signal, and Price Positioning.
Entries are triggered when the cumulative weight of bullish signals exceeds a specified buy threshold, indicating a strong uptrend or potential trend reversal.
Exits are initiated when the cumulative weight of bearish signals surpasses a specified sell threshold, or when additional conditions such as consolidation patterns or ATR-based targets are met.
There are various exit types that you can choose between, which can be used separately or in conjunction with one another. As an example you might want to exit on a different condition during consolidation periods than during other periods or just use ATR with some other backstop.
They are listed in evaluation order i.e. ATR trumps all, Consolidation exit trumps the regular Kumo sell and so on:
**ATR Sell**: Exits trades based on ATR-based profit targets and stop-losses.
**Consolidation Exit**: Exits trades during consolidation periods to reduce drawdown.
**Sell Below Kumo**: Exits trades when the price is below the Kumo, indicating a potential downtrend.
**Sell Threshold**: Exits trades when the cumulative weight of bearish signals surpasses a specified sell threshold.
There are various 'filters' which are really behavior modifiers:
**Kumo Breakout Filter**: Requires price to close above the Kumo for buy signals (essentially a entry delay).
**Whipsaw Filter**: Ensures trend strength over specified days to reduce false signals.
**Buy Cooldown**: Prevents new entries until half the Kijun period passes after an exit (prevents flapping).
**Chikou Filter**: Delays exits unless the previous close is below the Chikou Span.
**Consolidation Trend Filter**: Prevents consolidation exits if the trend is bullish (rare, but happens).
Then there are some debugging options. Ichimoku periods have some presets (personally I like 8/22/44/22) but are freely configurable, preset to the traditional values for purists.
The list of signals and most thresholds follow, play around with them. Thats all.
Cheers,
**Core Ichimoku Signals**
**Kumo Breakout**
- 30 (Bullish) / -30 (Bearish)
- Indicates a strong trend when the price breaks above (bullish) or below (bearish) the Kumo (cloud). This signal suggests a significant shift in market sentiment.
**Chikou Cross**
- 20 (Bullish) / -20 (Bearish)
- Shows the relationship between the Chikou Span (lagging span) and the current price. A bullish signal occurs when the Chikou Span is above the price, indicating a potential uptrend. Conversely, a bearish signal occurs when the Chikou Span is below the price, suggesting a downtrend.
**Kijun Cross**
- 15 (Bullish) / -15 (Bearish)
- Signals trend changes when the Tenkan-sen (conversion line) crosses above (bullish) or below (bearish) the Kijun-sen (base line). This crossover is often used to identify potential trend reversals.
**Tenkan Cross**
- 10 (Bullish) / -10 (Bearish)
- Indicates short-term trend changes when the price crosses above (bullish) or below (bearish) the Tenkan-sen. This signal helps identify minor trend shifts within the broader trend.
**Kumo Twist**
- 5 (Bullish) / -5 (Bearish)
- Shows changes in the Kumo's direction, indicating potential trend shifts. A bullish Kumo Twist occurs when Senkou Span A crosses above Senkou Span B, and a bearish twist occurs when Senkou Span A crosses below Senkou Span B.
**Additional Signals**
**Kijun Confirmation**
- 8 (Bullish) / -8 (Bearish)
- Confirms the trend based on the price's position relative to the Kijun-sen. A bullish signal occurs when the price is above the Kijun-sen, and a bearish signal occurs when the price is below it.
**Tenkan-Kijun Above Cloud**
- 5 (Bullish) / -5 (Bearish)
- Indicates a strong bullish trend when both the Tenkan-sen and Kijun-sen are above the Kumo. Conversely, a bearish signal occurs when both lines are below the Kumo.
**Chikou Above Cloud**
- 5 (Bullish) / -5 (Bearish)
- Shows the Chikou Span's position relative to the Kumo, indicating trend strength. A bullish signal occurs when the Chikou Span is above the Kumo, and a bearish signal occurs when it is below.
**Price-Kijun Cross**
- 2 (Bullish) / -2 (Bearish)
- Signals short-term trend changes when the price crosses above (bullish) or below (bearish) the Kijun-sen. This signal is similar to the Kijun Cross but focuses on the price's direct interaction with the Kijun-sen.
**Chikou Span Signal**
- 10 (Bullish) / -10 (Bearish)
- Indicates the trend based on the Chikou Span's position relative to past price highs and lows. A bullish signal occurs when the Chikou Span is above the highest high of the past period, and a bearish signal occurs when it is below the lowest low.
**Price Positioning**
- 10 (Bullish) / -10 (Bearish)
- Shows indecision when the price is between the Tenkan-sen and Kijun-sen, indicating a potential consolidation phase. A bullish signal occurs when the price is above both lines, and a bearish signal occurs when the price is below both lines.
**Confidence Level**: Highly Sensitive
- **Buy Threshold**: 50
- **Sell Threshold**: -50
- **Notes / Significance**: ~2–3 signals, very early trend detection. High sensitivity, may capture noise and false signals.
**Confidence Level**: Entry-Level
- **Buy Threshold**: 58
- **Sell Threshold**: -58
- **Notes / Significance**: ~3–4 signals, often Chikou Cross or Kumo Breakout. Very sensitive, risks noise (e.g., false buys in choppy markets).
**Confidence Level**: Entry-Level
- **Buy Threshold**: 60
- **Sell Threshold**: -60
- **Notes / Significance**: ~3–4 signals, Kumo Breakout or Chikou Cross anchors. Entry point for early trends.
**Confidence Level**: Moderate
- **Buy Threshold**: 65
- **Sell Threshold**: -65
- **Notes / Significance**: ~4–5 signals, balances sensitivity and reliability. Suitable for moderate risk tolerance.
**Confidence Level**: Conservative
- **Buy Threshold**: 70
- **Sell Threshold**: -70
- **Notes / Significance**: ~4–5 signals, emphasizes stronger confirmations. Reduces false signals but may miss some opportunities.
**Confidence Level**: Very Conservative
- **Buy Threshold**: 75
- **Sell Threshold**: -75
- **Notes / Significance**: ~5–6 signals, prioritizes high confidence. Minimizes risk but may enter trades late.
**Confidence Level**: High Confidence
- **Buy Threshold**: 80
- **Sell Threshold**: -80
- **Notes / Significance**: ~6–7 signals, very strong confirmations needed. Suitable for cautious traders.
**Confidence Level**: Very High Confidence
- **Buy Threshold**: 85
- **Sell Threshold**: -85
- **Notes / Significance**: ~7–8 signals, extremely high confidence required. Minimizes false signals significantly.
**Confidence Level**: Maximum Confidence
- **Buy Threshold**: 90
- **Sell Threshold**: -90
- **Notes / Significance**: ~8–9 signals, maximum confidence level. Ensures trades are highly reliable but may result in fewer trades.
**Confidence Level**: Ultra Conservative
- **Buy Threshold**: 100
- **Sell Threshold**: -100
- **Notes / Significance**: ~9–10 signals, ultra-high confidence. Trades are extremely reliable but opportunities are rare.
**Confidence Level**: Extreme Confidence
- **Buy Threshold**: 110
- **Sell Threshold**: -110
- **Notes / Significance**: All signals align, extreme confidence. Trades are almost certain but very few opportunities.
Arena-Hub-DC-Strategy V3.1This script must be individually configured for each cryptocurrency. After monitoring several coins, I’ve realized that each one requires its own unique setup. There's no “one-size-fits-all” — and different timeframes require different configurations as well.
⚠️ Risk management is essential.
If you're not familiar with proper risk management, please do not use this script. Make sure to configure your commission and slippage settings appropriately, as these are critical for realistic backtesting results. The Stop Loss and Take Profit levels are not automated — they must be adjusted by the user.
This script is not a financial advisor. It won't make risk or profit-related decisions for you. It's a tool designed to help identify potential entries, trends, and exit opportunities — but all final decisions must be made by the trader.
The default settings are only examples. You’ll need to customize them for each crypto asset and timeframe to make the strategy truly work for your style and market conditions.
The script evaluates:
The positioning of two RSIs relative to each other
Their alignment with a customizable RSI-EMA
The values of EMAs and the ATR (volatility)
A custom weighting system using ADR and VOLUME, which strongly affects trade signals. The weights can be adjusted in 0.1 increments, and even small changes can have a big impact — so fine-tuning is important!
These indicators were chosen because they complement each other:
RSI and its EMA help identify momentum shifts
ATR gauges volatility to confirm market conditions
ADR and VOLUME help filter weak signals and fine-tune entries and exits
🔍 Important: Only use this script if you understand how RSI, EMA, ATR, ADR, and VOLUME indicators work, and are comfortable making your own trading decisions.
The backtest results are based on historical data — the script cannot see the future, not even guess it. Please use it responsibly.
This script is an advanced trend-following strategy that dynamically combines RSI, SMA, EMA, ATR, ADX, and volume indicators using a unique weighting and filtering mechanism. Instead of simply combining traditional indicators, it applies them in a unique way:
✅ Dual RSI Comparison: The strategy utilizes two RSI indicators, analyzing their relative movement to filter out false signals and provide more precise entry points.
✅ Custom Entry and Exit Rules: EMA crossovers alone do not generate signals; instead, they go through a dynamic RSI filter that takes market volatility into account using ATR and ADX.
✅ Intelligent Trend Identification: Instead of standard moving averages, a uniquely weighted SMA/EMA system is used to assess trend strength and stability.
✅ ATR, ADX & Volume-Based Weighting: The EMA length is dynamically adjusted based on ATR, ADX, and volume, allowing moving averages to react faster in strong trends while smoothing out in choppy markets.
Advanced Dynamic EMA Zone
This is not your typical EMA indicator. It's an enhanced, dynamically adaptive trend zone that:
✅ Applies gradient shading – The zone between EMAs is divided into four layers, highlighting trend strength through smooth color transitions.
✅ Visualizes trend intensity – The strongest trends appear in the darkest shades, while weaker moves fade into lighter tones.
✅ Brings moving averages to life – Instead of static lines, it creates a visually intuitive trend channel.
✅ Differentiates bullish & bearish phases – The cloud fades from dark green to light green during an uptrend and from dark red to light red in a downtrend.
✅ Filters out market noise – Weakening trends appear more transparent, instantly revealing when momentum starts to fade.
✅ Enhances decision-making – Crossovers alone are not trading signals, but the visual representation helps identify market conditions at a glance.
➡️ What makes it unique?
Traditional moving average indicators rely on basic lines, but this is a full-fledged trend visualization system, helping traders filter noise and better understand price momentum.
🔄 Improved Custom EMA Smoothing Control
We’ve enhanced the weighting factor input for better user control! Previously, the EMA smoothing factor (ema1_smooth_factor) had a fixed step size that limited precision. Now, users can fine-tune it in 0.1 increments for greater flexibility.
✅ What’s new?
More precise control over EMA smoothing with adjustable step size (step=0.1).
Better adaptability to different market conditions.
Smoother trend visualization for traders who prefer fine-tuned settings.
This update ensures our custom EMA visualization remains superior to standard indicators. 🎯🔥
VBSMI Strategy by QTX Algo SystemsVolatility Based SMI Strategy by QTX Algo Systems
Overview
The Volatility Based SMI Strategy transforms our popular VBSMI with Dynamic Bands indicator into a fully automated strategy that traders can backtest inside TradingView. It retains all core logic from the indicator—including adaptive volatility scaling and trend-based overbought/oversold thresholds—but adds two configurable entry methods, exit conditions, and a dual-mode trade execution engine.
This script is published separately from the VBSMI indicator because some traders use VBSMI as a confluence tool within their existing system, while others prefer a rules-based strategy that can be simulated, optimized, and tracked over time. This script serves the latter use case.
How It Works
Like the original indicator, this strategy uses:
Double-Smoothed SMI Calculation: Based on smoothed momentum using EMA of the relative and full range.
Adaptive Volatility Scaling: Uses a normalized BBWP-based factor to reflect current market volatility.
Dynamic Band Adjustment: Trend direction and strength shift overbought/oversold levels upward or downward.
Band Tilt & Compression Controls: Inputs allow users to define how aggressively the bands shift with trend conditions.
What’s different is the strategy layer—you now choose from two types of entry and exit logic, and two execution styles.
🛠️ Entry & Exit Modes
There are two logic modes for both entry and exit, allowing you to adapt the strategy to your own philosophy:
Cross Mode (SMI Crosses EMA):
Entry: Buy when SMI crosses above its EMA
Exit: Close when SMI crosses below its EMA
Exit OB/OS Mode (Band Exit Logic):
Entry: Buy when price exits dynamic oversold zone (crosses back above tilted oversold band)
Exit: Close when price exits dynamic overbought zone (crosses back below tilted overbought band)
You can mix and match the modes (e.g., enter on Cross, exit on Band Exit).
⚙️ Spot vs. Leverage Mode
Spot Mode
Designed for traders who prefer long-only setups
Enters a long position and holds until the exit condition is met
Prevents overlapping trades—ensures only one position at a time
Leverage Mode
Designed for those testing bi-directional systems (e.g., long/short switching)
Automatically flips between long and short entries depending on the signals
Useful for testing symmetrical strategies or inverse conditions
Both modes work across any asset class and timeframe.
Customization Options
Users can adjust:
Smoothing K/D: Controls how fast or slow the momentum reacts
SMI EMA Length: Determines the responsiveness of the signal line
Trend Lookback Period: Influences how stable the dynamic band tilt is
Band Tilt & Compression Strengths: Refines how far bands adjust based on trend
Entry/Exit Logic Type: Choose between “Cross” or “Exit OB/OS” logic
Trading Mode: Select either "Spot" or "Leverage" depending on your use case
Why It’s Published Separately
This script is not a cosmetic or minor variation of the original indicator. It introduces:
Entry/exit logic
Order execution
Strategy testing capabilities
Mode selection (Spot vs. Leverage)
Signal logic control (Cross vs. Band Exit)
Because the original VBSMI indicator is widely used as a charting and confirmation tool, converting it into a strategy changes how it functions. This version is intended for strategy evaluation and automation, while the original remains available for discretionary and visual use.
Use Cases
This strategy is best suited for:
Evaluating VBSMI-based signals in backtests
Comparing entry and exit logic over time
Testing setups on different assets and timeframes
Automating VBSMI-based logic in a structured and risk-aware framework
Disclaimer
This strategy is for educational purposes only. It does not guarantee future results or profitability. Always test in simulation before using any strategy live, and use proper risk management and trade discipline.
BONK/USD (1H) - $4k DCA + Dual Trailing + Date FilterThis strategy trades BONK/USD on the 1-hour chart, employing a Dollar-Cost Averaging (DCA) approach for long entries.
It initiates a Base Order when a faster Exponential Moving Average (EMA) crosses above a slower one (signaling a potential uptrend, default 9/21 EMA). If the price declines after entry, it can automatically place up to two additional Safety Orders at predetermined lower levels, calculated using either Average True Range (ATR) volatility or fixed percentage drops.
Exits are triggered by a trend reversal (EMA crossunder) or a dual trailing stop-loss mechanism, which includes both a standard trail and a tighter profit-locking trail activated after reaching a certain profit target.
The strategy includes user-configurable inputs for all key parameters (EMAs, order sizes, trailing stops, SO spacing) and an optional date filter to limit backtesting or execution to a specific period. It also generates alerts formatted for potential automation with platforms like 3Commas.
BONK 1H Long Volatility StrategyGrok 1hr bonk strategy:
Key Changes and Why They’re Made
1. Indicator Adjustments
Moving Averages:
Fast MA: Changed to 5 periods (from, e.g., 9 on a higher timeframe).
Slow MA: Changed to 13 periods (from, e.g., 21).
Why: Shorter periods make the moving averages more sensitive to quick price changes on the 1-hour chart, helping identify trends faster.
ATR (Average True Range):
Length: Set to 10 periods (down from, e.g., 14).
Multiplier: Reduced to 1.5 (from, e.g., 2.0).
Why: A shorter ATR length tracks recent volatility better, and a lower multiplier lets the strategy catch smaller price swings, which are more common hourly.
RSI:
Kept at 14 periods with an overbought level of 70.
Why: RSI stays the same to filter out overbought conditions, maintaining consistency with the original strategy.
2. Entry Conditions
Trend: Requires the fast MA to be above the slow MA, ensuring a bullish direction.
Volatility: The candle’s range (high - low) must exceed 1.5 times the ATR, confirming a significant move.
Momentum: RSI must be below 70, avoiding entries at potential peaks.
Price: The close must be above the fast MA, signaling a pullback or trend continuation.
Why: These conditions are tightened to capture frequent volatility spikes while filtering out noise, which is more prevalent on a 1-hour chart.
3. Exit Strategy
Profit Target: Default is 5% (adjustable from 3-7%).
Stop-Loss: Default is 3% (adjustable from 1-5%).
Why: These levels remain conservative to lock in gains quickly and limit losses, suitable for the faster pace of a 1-hour timeframe.
4. Risk Management
The strategy may trigger more trades on a 1-hour chart. To avoid overtrading:
The ATR filter ensures only volatile moves are traded.
Trading fees (e.g., 0.5% on Coinbase) reduce the net profit to ~4% on winners and -3.5% on losers, requiring a win rate above 47% for profitability.
Suggestion: Risk only 1-2% of your capital per trade to manage exposure.
5. Visuals and Alerts
Plots: Blue fast MA, red slow MA, and green triangles for buy signals.
Alerts: Trigger when an entry condition is met, so you don’t need to watch the chart constantly.
How to Use the Strategy
Setup:
Load TradingView, select BONK/USD on the 1-hour chart (Coinbase pair).
Paste the script into the Pine Editor and add it to your chart.
Customize:
Adjust the profit target (e.g., 5%) and stop-loss (e.g., 3%) to your preference.
Tweak ATR or MA lengths if BONK’s volatility shifts.
Trade:
Look for green triangle signals and confirm with market context (e.g., volume or news).
Enter trades manually or via TradingView’s broker tools if supported.
Exit when the profit target or stop-loss is hit.
Test:
Use TradingView’s Strategy Tester to backtest on historical data and refine settings.
Benefits of the 1-Hour Timeframe
Faster Opportunities: Captures shorter-term uptrends in BONK’s volatile price action.
Responsive: Adjusted indicators react quickly to hourly changes.
Conservative: Maintains the 3-7% profit goal with tight risk control.
Potential Challenges
Noise: The 1-hour chart has more false signals. The ATR and MA filters help, but caution is needed.
Fees: Frequent trading increases costs, so ensure each trade’s potential justifies the expense.
Volatility: BONK can move unpredictably—monitor broader market trends or Solana ecosystem news.
Final Thoughts
Switching to a 1-hour timeframe makes the strategy more active, targeting shorter volatility spikes while keeping profits conservative at 3-7%. The adjusted indicators and conditions balance responsiveness with reliability. Backtest it on TradingView to confirm it suits BONK’s behavior, and always use proper risk management, as meme coins are highly speculative.
Disclaimer: This is for educational purposes, not financial advice. Cryptocurrency trading, especially with assets like BONK, is risky. Test thoroughly and trade responsibly.
Daily Bollinger Band StrategyOverview of the Daily Bollinger Band Strategy
1. Strategy Overview and Features
This strategy is a tool for backtesting a trading method that uses Bollinger Bands. It is *not* a tool for automated trading.
1-1. Main Display Items
The main chart displays the Bollinger Bands and the 200-day moving average.
It also shows the entry and exit points along with the position size (in units of 100 shares).
1-2. Summary of Trading Rules
For long (buy) strategies, the trade enters when the price crosses above the +1σ line of the Bollinger Bands, aiming to ride an upward trend. The position is exited when the price crosses below the middle band.
For short (sell) strategies, the trade enters when the price crosses below the -1σ line of the Bollinger Bands, aiming to ride a downward trend. The position is exited when the price crosses above the middle band.
1-3. Strategic Enhancements
The strategy uses the slope of the 200-day moving average to determine the trend direction and enter trades accordingly. This improves the win rate and payoff ratio.
Additionally, to reduce the probability of ruin, the risk per trade is limited to 1.0% of capital, and position sizing is adjusted using ATR (a volatility indicator).
2. Trading Rules
2-1. Chart Type
Only daily charts are used.
2-2. Indicators Used
(1) Bollinger Bands** (used for entry and exit signals)
- Period: Fixed at 80 days
- Upper and lower bands: Fixed at ±1σ
(2) Moving Average** (used to determine trend direction)
- Period: Fixed at 200 days
- Trend direction is judged based on whether the difference from the previous day is positive (upward) or negative (downward)
2-3. Buy Rules
Setup:
- Price crosses above the +1σ line from below
- Both the middle band and 200-day moving average are upward sloping
Entry:
- Buy at the next day’s market open using a market order
Exit:
- If the price crosses below the middle band, sell at the next day’s open using a market order
2-4. Sell Rules
Setup:
- Price crosses below the -1σ line from above
- Both the middle band and 200-day moving average are downward sloping
Entry:
- Sell at the next day’s market open using a market order
Exit:
- If the price crosses above the middle band, buy back at the next day’s open using a market order
2-5. Risk Management Rules
- Risk per trade: 1.0% of total capital (acceptable loss = capital × 1.0%)
- Position size: Acceptable loss ÷ 2ATR (rounded down to the nearest unit of 100 shares)
2-6. Other Notes
- No brokerage fees
- No pyramiding
- No partial exits
- No reverse positions (no “stop-and-reverse” trades)
3. Strategy Parameters
The following settings can be specified:
3-1. Period Settings
- Start date: Set the start date for the backtest period
- Stop date: Set the end date for the backtest period
3-2. Display of Trend and Signals
- Show trend: When checked, the background color of the bars is light red for an uptrend and light blue for a downtrend
- Show signal: When checked, entry and exit signals are displayed (note: signals are executed at the next day’s open, so there is a one-day lag in the display)
3-3. Capital Management Settings
- Funds: Capital available for trading (in JPY)
- Risk rate: Specify what percentage of the capital to risk per trade
Settings in the “Properties” tab are not used in this strategy.
4. Backtest Results (Example)
Here are the backtest results conducted by the author:
- Target Stocks: All components of the Nikkei 225
- Test Period: January 4, 2000 – December 30, 2024
- Data Points: 12,886
- Win Rate: 33.45%
- Net Profit: ¥82,132,380
- Payoff Ratio: 2.450
- Expected Value: ¥6,373.8
- Risk Rate: 1.0%
- Probability of Ruin: 0.00%
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デイリー・ボリンジャーバンド・ストラテジーの概要
1. ストラテジーの概要と特徴
このストラテジーは、ボリンジャーバンドを使ったトレード手法のバックテストを行うツールです。自動売買を行うツールではありません。
1-1. 主な表示項目
メインチャートにボリンジャーバンドと 200日移動平均線を表示します。
また、エントリーと手仕舞いのタイミングと数量（100株単位）も表示されます。
1-2. トレードルールの概要
買い戦略の場合、ボリンジャーバンドの +1σ 超えでエントリーして上昇トレンドに乗り、ミドルバンドを割ったら決済します。
売り戦略の場合、ボリンジャーバンドの -1σ 割りでエントリーして下降トレンドに乗り、ミドルバンドを上抜けたら決済します。
1-3. ストラテジーの工夫点
200日移動平均線の傾きを見てトレンド方向にエントリーをしています。こうして勝率とペイオフレシオの成績を向上しています。
また、破産確率を抑えるために、リスク資金比率を 1.0% にして、ATR（ボラティリティ指標） を使って注文数を調整しています。
2. 売買ルール
2-1. 使用するチャート
日足チャートに限定します
2-2. 使用する指標
(1) ボリンジャーバンド（仕掛けと手仕舞いのシグナルに使用）
期間は80日に固定
上下バンドは ±1σ に固定
(2) 移動平均線（トレンドの方向を見るために使用）
期間は200日に固定
移動平均の値の前日との差がプラスのとき上向き、マイナスのとき下向きと判断
2-3. 買いのルール
セットアップ：ボリンジャーバンドの +1σ を価格が下から上に交差 かつ ミドルバンドと 200日移動平均線が上向き
仕掛け：翌日の寄り付きに成行で買う
手仕舞い：ボリンジャーバンドのミドルバンドを価格が上から下に交差したら、翌日の寄り付きに成行で売る
2-4. 売りのルール
セットアップ：ボリンジャーバンドの -1σ を価格が上から下に交差 かつ ミドルバンドと 200日移動平均線が下向き
仕掛け：翌日の寄り付きに成行で売る
手仕舞い：ボリンジャーバンドのミドルバンドを価格が下から上に交差したら、翌日の寄り付きに成行で買い戻す
2-5. 資金管理のルール
リスク資金比率：資産の 1.0%（許容損失 = 資産 × 1.0%）
注文数：許容損失 ÷ 2ATR（単元株数未満は切り捨て）
2-6. その他
仲介手数料：なし
ピラミッディング：なし
分割決済：なし
ドテン：しない
3. ストラテジーのパラメーター
次の項目が指定できます。
3-1. 期間の設定
Staer date : バックテストの検証期間の開始日を指定します
Stop date : バックテストの検証期間の終了日を指定します
3-2. トレンドとシグナルの表示
Show trend : チェックを入れると、バーの背景色が、トレンドが上昇のときは薄い赤で、下落のときは薄い青で表示されます
Show signal : チェックを入れると、エントリーと手仕舞いのシグナルを表示します（シグナルの出た翌日の寄り付きに売買をするので表示に1日のずれがあります）
3-3. 資金管理用の設定
Funds : トレード用の資金(円)
Risk rate : 許容損失を資金の何%にするかで指定します
「プロパティタブ」で設定する値は、このストラテジーでは有効ではありません。
4. バックテストの結果（例）
作者がバックテストを実施した結果をお知らせします。
対象銘柄：日経225構成銘柄すべて
対象期間：2000年1月4日～2024年12月30日
データ件数：12,886
勝率：33.45%
純利益：82,132,380
ペイオフレシオ：2.450
期待値：6,373.8
リスク資金比率：1.0%
破産確率：0.00%
BB Breakout + Momentum Squeeze [Strategy]This Strategy is Based on 3 free indicators
- Bollinger Bands Breakout Oscillator: Link
- TTM Squeeze Pro: Link
- Rolling ATR Bands: Link
Bollinger Bands Breakout Oscillator - This tool shows how strong a market trend is by measuring how often prices move outside their normal Bollinger bands range. It helps you see whether prices are strongly moving in one direction or just moving sideways. By looking at how much and how frequently prices push beyond their typical boundaries, you can identify which direction the market is heading over your selected time period.
TM Squeeze Pro - This is a custom version of the TTM Squeeze indicator.
It's designed to help traders spot consolidation phases in the market (when price is coiling or "squeezing") and to catch breakouts early when volatility returns. The logic is based on the relationship between Bollinger Bands and Keltner Channels, combined with a momentum oscillator to show direction and strength.
Rolling ATR Bands - This indicator combines volatility bands (ATR) with momentum and trend signals to show where the market might be breaking out, retesting, or trending. It's highly visual and helpful for traders looking to time entries/exits during trending or volatile moves.
Logic Of the Strategy:
We are going to use the Bollinger Bands Breakout to determine the direction of the market. Than check the Volatility of the price by looking at the TTM Squeeze indicator. And use the ATR Bands to determine dynamic Stop Losses and based on the calculate the Take Profit targets and quantity for each position dynamically.
For the Long Setup:
1. We need to see the that Bull Power (Green line of the Bollinger Bands Breakout Oscilator) is crossing the level of 50.
2. Check the presence of volatility (Green dot based on the TTM Squeeze indicator)
For the Short Setup:
1. We need to see the that Bear Power (Red line of the Bollinger Bands Breakout Oscilator) is crossing the level of 50.
2. Check the presence of volatility (Green dot based on the TTM Squeeze indicator)
Stop Loss is determined by the Lower ATR Band (for the Long entry) and Upper ATR Band (For the Short entry)
Take Profit is 1:1.5 risk reward ration, which means if the Stop loss is 1% the TP target will be 1.5%
Move stop Loss to Breakeven: If the price will go in the direction of the trade for at least half of the Risk Reward target then the stop will automatically be adjusted to the entry price. For Example: the Stop Loss is 1%, the price has move at least 0.5% in the direction of your trade and that will move the Stop Loss level to the Entry point.
You can Adjust the parameters for each indicator used in that script and also adjust the Risk and Money management block to see how the PnL will change.
