Roboquant RP Profits NY Open Retest StrategyRoboquant RP Profits NY Open Retest Strategy A good strategy for CL
基本面分析
US/SPY- Financial Regime Index Swing Strategy Credits: concept inspired by EdgeTools Bloomberg Financial Conditions Index (Proxy)
Improvements: eight component basket, inverse volatility weights, winsorization option( statistical technique used to limit the influence of outliers in a dataset by replacing extreme values with less extreme ones, rather than removing them entirely), slope and price gates, exit guards, table and gradients.
Summary in one paragraph
A macro regime swing strategy for index ETFs, futures, FX majors, and large cap equities on daily calculation with optional lower time execution. It acts only when a composite Financial Conditions proxy plus slope and an optional price filter align. Originality comes from an eight component macro basket with inverse volatility weights and winsorized return z scores that produce a portable yardstick.
Scope and intent
Markets: SPY and peers, ES futures, ACWI, liquid FX majors, BTC, large cap equities.
Timeframes: calculation daily by default, trade on any chart.
Default demo: SPY on Daily.
Purpose: convert broad financial conditions into clear swing bias and exits.
Originality and usefulness
Unique fusion: return z scores for eight liquid proxies with inverse volatility weighting and optional winsorization, then slope and price gates.
Failure mode addressed: false starts in chop and early shorts during easy liquidity.
Testability: all knobs are inputs and the table shows components and weights.
Portable yardstick: z scores center at zero so thresholds transfer across symbols.
Method overview in plain language
Base measures
Return basis: natural log return over a configurable window, standardized to a z score. Winsorization optional to cap extremes.
Components
EQ US and EQ GLB measure equity tone.
CREDIT uses LQD over HYG. Higher credit quality outperformance is risk off so sign is flipped after z score.
RATES2Y uses two year yield, sign flipped.
SLOPE uses ten minus two year yield spread.
USD uses DXY, sign flipped.
VOL uses VIX, sign flipped.
LIQ uses BIL over SPY, sign flipped.
Each component is smoothed by the composite EMA.
Fusion rule
Weighted sum where weights are equal or inverse volatility with exponent gamma, normalized to percent so they sum to one.
Signal rule
Long when composite crosses up the long threshold and its slope is positive and price is above the SMA filter, or when composite is above the configured always long floor.
Short when composite crosses down the short threshold and its slope is negative and price is below the SMA filter.
Long exit on cross down of the long exit line or on a fresh short signal.
Short exit on cross up of the short exit line or on a fresh long signal, or when composite falls below the force short exit guard.
What you will see on the chart
Markers on suggestion bars: L for long, S for short, LX and SX for exits.
Reference lines at zero and soft regime bands at plus one and minus one.
Optional background gradient by regime intensity.
Compact table with component z, weight percent, and composite readout.
Table fields and quick reading guide
Component: EQ US, EQ GLB, CREDIT, RATES2Y, SLOPE, USD, VOL, LIQ.
Z: current standardized value, green for positive risk tone where applicable.
Weight: contribution percent after normalization.
Composite: current index value.
Reading tip: a broadly green Z column with slope positive often precedes better long context.
Inputs with guidance
Setup
Calc timeframe: default Daily. Leave blank to inherit chart.
Lookback: 50 to 1500. Larger length stabilizes regimes and delays turns.
EMA smoothing: 1 to 200. Higher smooths noise and delays signals.
Normalization
Winsorize z at ±3: caps extremes to reduce one off shocks.
Return window for equities: 5 to 260. Shorter reacts faster.
Weighting
Weight lookback: 20 to 520.
Weight mode: Equal or InvVol.
InvVol exponent gamma: 0.1 to 3. Higher compresses noisy components more.
Signals
Trade side: Long Short or Both.
Entry threshold long and short: portable z thresholds.
Exit line long and short: soft exits that give back less.
Slope lookback bars: 1 to 20.
Always long floor bfci ≥ X: macro easy mode keep long.
Force short exit when bfci < Y: macro stress guard.
Confirm
Use price trend filter and Price SMA length.
View
Glow line and Show component table.
Symbols
SPY ACWI HYG LQD VIX DXY US02Y US10Y BIL are defaults and can be changed.
Realism and responsible publication
No performance claims. Past is not future.
Shapes can move intrabar and settle on close.
Execution is on standard candles only.
Honest limitations and failure modes
Major economic releases and illiquid sessions can break assumptions.
Very quiet regimes reduce contrast. Use longer windows or higher thresholds.
Component proxies are ETFs and indexes and cannot match a proprietary FCI exactly.
Strategy notice
Orders are simulated on standard candles. All security calls use lookahead off. Nonstandard chart types are not supported for strategies.
Entries and exits
Long rule: bfci cross above long threshold with positive slope and optional price filter OR bfci above the always long floor.
Short rule: bfci cross below short threshold with negative slope and optional price filter.
Exit rules: long exit on bfci cross below long exit or on a short signal. Short exit on bfci cross above short exit or on a long signal or on force close guard.
Position sizing
Percent of equity by default. Keep target risk per trade low. One percent is a sensible starting point. For this example we used 3% of the total capital
Commisions
We used a 0.05% comission and 5 tick slippage
Legal
Education and research only. Not investment advice. Test in simulation first. Use realistic costs.
Confluence Dashboard + Strategy [Daily + Weekly Adaptive]Removed duplicate strategy() declarations
Scoped getWeeklyBias() safely with correct request.security() usage
Ensured all variables are declared before use
Aligned background shading with bias logic
Streamlined signal tier logic to avoid overlap
Integrated strategy entries/exits cleanly
DCA with the Money Supply Index DCA with the Money Supply Index (MSI) by zdmre
This strategy is based on the Money Supply Index (MSI) by zdmre and enhances it with two functional options for users: a DCA (Dollar-Cost Averaging) approach and a signal-based buy/sell mode. It’s designed to help traders and investors make data-driven, disciplined entry decisions based on monetary supply trends.
🧠 Concept Overview
The Money Supply Index (MSI) provides insight into how liquidity (money supply) influences market movements. This strategy builds upon that foundation by allowing users to either:
Accumulate positions over time using DCA, based on favorable MSI conditions.
Execute a single buy and sell trade, optimized for bull market conditions.
⚙️ Inputs Explained
General Parameters
Start Bar Index / Stop Bar Index
Defines the range of bars (historical data) for backtesting or strategy visualization.
Long DCA
Activates the DCA mode. If unchecked, the strategy operates in single-entry/single-exit signal mode.
Trading Signal
Enables signal-based entries and exits when the MSI reaches predefined thresholds.
DCA Parameters
Entry Value
The MSI value that triggers a DCA buy event. When the MSI crosses below this value, the strategy considers it a favorable moment to deploy the saved capital.
Saved Amount
The amount of money set aside regularly (e.g., monthly) for investment. This simulates the DCA effect by accumulating capital and deploying it when conditions are optimal.
Data Inputs
Money Supply
The data source for the Money Supply Index (default: ECONOMICS:USM2).
Relational Symbol
The market instrument to compare against the money supply (default: NASDAQ_DLY:NDX). This allows the strategy to measure liquidity impact on a specific market.
Chart Display Options
You can toggle these metrics on the chart for better visualization:
Entry Price (green) – The price level of executed buys.
Cash Balance (yellow) – Remaining uninvested capital.
Invested Capital (red) – Total amount currently invested.
Current Value (blue) – The current valuation of the investment.
Profit (purple) – The total realized and unrealized profit.
Trades on Chart / Signal Labels / Quantity – Enables trade markers, signal text, and position size visualization.
📈 How the Strategy Works
1️⃣ DCA Mode
In DCA mode, the strategy simulates periodic savings and only invests when the MSI indicates favorable liquidity conditions (based on the Entry Value).
This approach aims to achieve the best possible average entry price over time — a powerful strategy for long-term investors seeking stable accumulation with reduced emotional bias.
2️⃣ Signal-Based Mode
In signal mode (with DCA disabled), the strategy performs one buy and one sell trade based on MSI turning points.
It’s most effective during bull markets, where liquidity expansion supports upward momentum.
This mode helps identify high-probability entry and exit zones rather than averaging in continuously.
💡 Additional Notes
This strategy includes helpful metrics to monitor your personal investment performance — showing invested capital, cash reserves, and profit in real-time.
The goal is to combine macroeconomic insight (money supply) with disciplined execution and capital management.
⚠️ Disclaimer
This strategy is for educational and research purposes only. It does not constitute financial advice. Always conduct your own analysis before making investment decisions.
Order Block Volumatic FVG StrategyInspired by: Volumatic Fair Value Gaps —
License: CC BY-NC-SA 4.0 (Creative Commons Attribution–NonCommercial–ShareAlike).
This script is a non-commercial derivative work that credits the original author and keeps the same license.
What this strategy does
This turns BigBeluga’s visual FVG concept into an entry/exit strategy. It scans bullish and bearish FVG boxes, measures how deep price has mitigated into a box (as a percentage), and opens a long/short when your mitigation threshold and filters are satisfied. Risk is managed with a fixed Stop Loss % and a Trailing Stop that activates only after a user-defined profit trigger.
Additions vs. the original indicator
✅ Strategy entries based on % mitigation into FVGs (long/short).
✅ Lower-TF volume split using upticks/downticks; fallback if LTF data is missing (distributes prior bar volume by close’s position in its H–L range) to avoid NaN/0.
✅ Per-FVG total volume filter (min/max) so you can skip weak boxes.
✅ Age filter (min bars since the FVG was created) to avoid fresh/immature boxes.
✅ Bull% / Bear% share filter (the 46%/53% numbers you see inside each FVG).
✅ Optional candle confirmation and cooldown between trades.
✅ Risk management: fixed SL % + Trailing Stop with a profit trigger (doesn’t trail until your trigger is reached).
✅ Pine v6 safety: no unsupported args, no indexof/clamp/when, reverse-index deletes, guards against zero/NaN.
How a trade is decided (logic overview)
Detect FVGs (same rules as the original visual logic).
For each FVG currently intersected by the bar, compute:
Mitigation % (how deep price has entered the box).
Bull%/Bear% split (internal volume share).
Total volume (printed on the box) from LTF aggregation or fallback.
Age (bars) since the box was created.
Apply your filters:
Mitigation ≥ Long/Short threshold.
Volume between your min and max (if enabled).
Age ≥ min bars (if enabled).
Bull% / Bear% within your limits (if enabled).
(Optional) the current candle must be in trade direction (confirm).
If multiple FVGs qualify on the same bar, the strategy uses the most recent one.
Enter long/short (no pyramiding).
Exit with:
Fixed Stop Loss %, and
Trailing Stop that only starts after price reaches your profit trigger %.
Input settings (quick guide)
Mitigation source: close or high/low. Use high/low for intrabar touches; close is stricter.
Mitigation % thresholds: minimal mitigation for Long and Short.
TOTAL Volume filter: skip FVGs with too little/too much total volume (per box).
Bull/Bear share filter: require, e.g., Long only if Bull% ≥ 50; avoid Short when Bull% is high (Short Bull% max).
Age filter (bars): e.g., ≥ 20–30 bars to avoid fresh boxes.
Confirm candle: require candle direction to match the trade.
Cooldown (bars): minimum bars between entries.
Risk:
Stop Loss % (fixed from entry price).
Activate trailing at +% profit (the trigger).
Trailing distance % (the trailing gap once active).
Lower-TF aggregation:
Auto: TF/Divisor → picks 1/3/5m automatically.
Fixed: choose 1/3/5/15m explicitly.
If LTF can’t be fetched, fallback allocates prior bar’s volume by its close position in the bar’s H–L.
Suggested starting presets (you should optimize per market)
Mitigation: 60–80% for both Long/Short.
Bull/Bear share:
Long: Bull% ≥ 50–70, Bear% ≤ 100.
Short: Bull% ≤ 60 (avoid shorting into strong support), Bear% ≥ 0–70 as you prefer.
Age: ≥ 20–30 bars.
Volume: pick a min that filters noise for your symbol/timeframe.
Risk: SL 4–6%, trailing trigger 1–2%, distance 1–2% (crypto example).
Set slippage/fees in Strategy Properties.
Notes, limitations & best practices
Data differences: The LTF split uses request.security_lower_tf. If the exchange/data feed has sparse LTF data, the fallback kicks in (it’s deliberate to avoid NaNs but is a heuristic).
Real-time vs backtest: The current bar can update until close; results on historical bars use closed data. Use “Bar Replay” to understand intrabar effects.
No pyramiding: Only one position at a time. Modify pyramiding in the header if you need scaling.
Assets: For spot/crypto, TradingView “volume” is exchange volume; in some markets it may be tick volume—interpret filters accordingly.
Risk disclosure: Past performance ≠ future results. Use appropriate position sizing and risk controls; this is not financial advice.
Credits
Visual FVG concept and original implementation: BigBeluga.
This derivative strategy adds entry/exit logic, volume/age/share filters, robust LTF handling, and risk management while preserving the original spirit.
License remains CC BY-NC-SA 4.0 (non-commercial, attribution required, share-alike).
KDJ – Long Only v3.0 (TradingView Strategy)
Overview｜概覽
EN
A research strategy that automates long-only entries using a KDJ-centric core with multi-layer confirmations and volatility-aware exits. Default preset targets ETH 5m; other symbols/timeframes can be tuned.
中文
研究用策略，透過 KDJ 核心與多層確認來自動化只做多進場，並以隨波動調整的出場邏輯運作。預設為 ETH 5 分鐘；其他商品/週期可自行調參。
Backtest (hypothetical) example: ETHUSDT.P, 5m, 2024-09-18→2025-09-18, fee 0.05%, slippage 1 tick.（僅示意，屬假設性回測）
What it does｜做什麼
EN
Signals are organized into channels:
A KDJ trend core
B OB/FVG touch pullback
CP Double-bottom (buffered neckline)
SR Support/Resistance bounce with rejection/zone checks
D EMA pullback (long EMA length)
E VWAP reclaim (lower-band pierce & recapture)
F Prior-low sweep & reclaim
中文
訊號分成多通道：
A KDJ 順勢核心
B OB/FVG 回踩觸價
CP 雙底（頸線含緩衝）
SR 支撐/阻力觸價不破（含拒絕與區域檢查）
D EMA 回踩（長週期 EMA）
E VWAP 收復（下緣穿越後收回）
F 前低掃回
High-level logic｜高層級原理
HTF/Mid-TF context：內建 5/15/1H 或 15/60/4H 組合；以簡化趨勢線/區域提供觸價參考
Trend & structure：本階 EMA(8/21/200) 結構；Structure Breakout（近期高低點 ±ATR 緩衝）／EMA8/21 回踩
Momentum/volume：MACD、KDJ 金叉與低區偵測、量能驗證
Regime：ADX 閘（趨勢/盤整門檻）、EMA 帶寬過濾震盪、Peak Guard 避免過度延伸
No look-ahead：入場不使用前視；樞紐/趨勢線僅作情境參考
Inputs & Features｜參數與功能
Market Preset：Generic / ETH（ETH 預設收緊若干門檻，開箱即用）
Entry Mode：KDJ_Core / CandleOnly / KDJ_and_Candle
Session Filter：最多三段交易時窗
Lite Filters：過度延伸、實體大小、DI 差距
S/R 模組：拒絕條件、KDJ 覆核、區域要求、即時 R:R 檢核
OrderBlock/FVG：近棒位移掃描
Chart Pattern：雙底 W，ATR 容差與頸線緩衝
Plotting：EMA200、通道字母標記、可選 TP/SL 標籤
Automation via Alerts（generic）｜快訊自動化（通用）
EN
On entries/exits the strategy emits JSON through alert_message. Create alerts with “Any alert() function call” and route them to your own webhook/bridge. Symbol mapping, sizing mode, and user info are configurable in inputs.
中文
進出場時透過 alert_message 輸出 JSON。建立快訊時選 “Any alert() function call”，再由你的 webhook/橋接服務轉單；輸入面板可設定商品代碼、下單型式與使用者資訊。
提示：調整參數後，請重建快訊，並將訊息欄設為 {{strategy.order.alert_message}}。
Position sizing｜部位大小
base / quote / percent_local / percent（percent_local 以本地 USD 估值計算）
可選「按數量模式」以便與本地部位同步（position_size sync）
Risk & Exits｜風險與出場
SL：ATR / Swing / ATR_or_Swing；TP Cap 以 ATR 或 % 限制上限
Breakeven & Trailing：達指定 R:R 啟動保本；之後以 最高價回看 − ATR×k 追蹤
Same-bar exits：可允許/禁止同根觸發 TP/SL
Pyramiding：pyramiding=2，最多兩筆多單可同時存在（淨倉交易所請留意整體倉位的平倉行為）
Suggested workflow｜建議流程
回測目標市場/週期 → 設定時段/濾網與門檻 → 微調 TP/SL 與部位大小 → 建立快訊（{{strategy.order.alert_message}}）→ 監看執行日誌
Notes & Disclaimer｜注意與免責
回測結果仰賴時間框解析與成交規則；棒內路徑與實盤可能不同
僅供研究/教育；非投資建議
本頁無廣告、無外部連結或聯絡資訊
Release Notes｜版本說明
2025-09-19
新增：One-shot Force Flat（一鍵清倉僅一次）— 於下一根收盤執行，完成後自動失效
Webhook：進/出場皆輸出 JSON；提醒更新參數後重建快訊
行為澄清：pyramiding=2，允許同圖表最多兩筆多單並存；同棒出場可設定
2025-09-18
Netted venue 說明：在淨倉模式下，出場會影響同商品的整體淨多倉；請留意手動單與策略單的互動
2025-08-28
修正小數顯示；預設優化（ETH/5m）；保留隨波動的 RR/SL 邏輯
Grand Master's Candlestick Dominance (ATR Enhanced)### Grand Master's Candlestick Dominance (ATR Enhanced)
**Overview**
Unleash the ancient wisdom of Japanese candlestick charting with a modern twist! This comprehensive Pine Script v5 strategy and indicator scans for over 75 classic and advanced candlestick patterns (bullish, bearish, and neutral), assigning dynamic strength scores (1-10) to each for precise signal filtering. Enhanced with Average True Range (ATR) for volatility-aware body size validation, it dominates the markets by combining timeless pattern recognition with robust confirmation layers. Whether used as a backtestable strategy or visual indicator, it empowers traders to spot high-probability reversals, continuations, and indecision setups with surgical accuracy.
Inspired by Steve Nison's *Japanese Candlestick Charting Techniques*, this tool elevates pattern analysis beyond basics—think Hammers, Engulfing patterns, Morning Stars, and rare gems like Abandoned Baby or Concealing Baby Swallow—all consolidated into intelligent arrays for real-time averaging and prioritization.
**Key Features**
- **Extensive Pattern Library**:
- **Bullish (25+ patterns)**: Hammer (8.0), Bullish Engulfing (10.0), Morning Star (7.0), Three White Soldiers (9.0), Dragonfly Doji (8.0), and more (e.g., Rising Three, Unique Three River Bottom).
- **Bearish (25+ patterns)**: Hanging Man (8.0), Bearish Engulfing (10.0), Evening Star (7.0), Three Black Crows (9.0), Gravestone Doji (8.0), and exotics like Upside Gap Two Crows or Stalled Pattern.
- **Neutral/Indecision (34+ patterns)**: Doji variants (Long-Legged, Four Price), Spinning Tops, Harami Crosses, and multi-bar setups like Upside Tasuki Gap or Advancing Block.
Each pattern includes duration tracking (1-5 bars) and ATR-adjusted body/shadow criteria for relevance in volatile conditions.
- **Smart Confirmation Filters** (All Toggleable):
- **Trend Alignment**: 20-period SMA (customizable) ensures entries align with the prevailing trend; optional higher timeframe (e.g., Daily) MA crossover for multi-timeframe confluence.
- **Support/Resistance (S/R)**: Pivot-based levels with 0.01% tolerance to confirm bounces or breaks.
- **Volume Surge**: 20-period volume MA with 1.5x spike multiplier to validate momentum.
- **ATR Body Sizing**: Filters small bodies (<0.3x ATR) and long bodies (>0.8x ATR) for context-aware pattern reliability.
- **Follow-Through**: Ensures post-pattern confirmation via bullish/bearish closes or closes beyond prior bars.
Minimum average strength (default 7.0) and individual pattern thresholds (5.0) prevent weak signals.
- **Entry & Exit Logic**:
- **Long Entry**: Bullish average strength ≥7.0 (outweighing bearish), uptrend, volume spike, near support, follow-through, and HTF alignment.
- **Short Entry**: Mirror for bearish dominance in downtrends near resistance.
- **Exits**: Bearish/neutral shift, or fixed TP (5%) / SL (2%)—pyramiding disabled, 10% equity sizing.
- Backtest range: Jan 1, 2020 – Dec 31, 2025 (editable). Initial capital: $10,000.
- **Interactive Dashboard** (Top-Right Panel):
Real-time insights including:
- Market phase (e.g., "Bullish Phase (Avg Str: 8.2)"), active pattern (e.g., "BULLISH: Bullish Engulfing (Str: 10.0, Bars: 2)"), and trend status.
- Strength breakdowns (Bull/Bear/Neutral counts & averages).
- Filter status (e.g., "Volume: ✔ Spike", "ATR: Enabled (L:0.8, S:0.3)").
- Backtest stats: Total trades, win rate, streak, and last entry/exit details (price & timestamp).
Toggle mode: Strategy (live trades) or Indicator (signals only).
- **Advanced Alerts** (15+ Toggleable Types):
Set up via TradingView's "Any alert() function call" for bar-close triggers:
- Entry/Exit signals with strength & pattern details.
- Strong patterns (≥2 bullish/bearish), neutral indecision, volume spikes.
- S/R breakouts, HTF reversals, high-confidence singles (≥8.0 strength).
- Conflicting signals, MA crossovers, ATR volatility bursts, multi-bar completions.
Example: "STRONG BULLISH PATTERN detected! Strength: 9.5 | Top Pattern: Three White Soldiers | Trend: Up".
**Customization & Usage Tips**
- **Inputs Groups**: Strategy toggles, confirmations, exits, backtest dates, and 15+ alert switches—all intuitively grouped.
- **Optimization**: Tune min strengths for aggressive (lower) or conservative (higher) trading; enable/disable filters to suit your style (e.g., disable S/R for scalping).
- **Best For**: Forex, stocks, crypto on 1H–Daily charts. Test on historical data to refine TP/SL.
- **Limitations**: No external data installs; relies on built-in TA functions. Patterns are probabilistic—combine with your risk management.
Master the candles like a grandmaster. Deploy on TradingView, backtest relentlessly, and let dominance begin! Questions? Drop a comment.
*Version: 1.0 | Updated: September 2025 | Credits: Built on Pine Script v5 with nods to Nison's timeless techniques.*
MS - Çoklu Onay Stratejisi (AL-SAT)"VOLUME, MA50, RSI, DMI, ATR
5 conditions, all turning positive at the same time gives a buy signal; one of them turning negative gives a sell signal. This should be evaluated with weekly data. Not financial advice."
FVG Ultra Assertive - Individual Filters (mtbr)FVG Ultra Assertive - Individual Filters (mtbr)
What this script offers:
This strategy detects and highlights FVGs (Fair Value Gaps) on the chart, providing traders with a visual and systematic approach to identify potential price inefficiencies. The script plots bullish and bearish FVG zones using customizable boxes and labels, allowing users to easily spot high-probability trading areas. In addition, it opens and closes simulated trades based on the detected FVGs, enabling full backtesting and strategy performance evaluation. It integrates multiple independent filters to validate the strength of each FVG signal before entering a trade.
How it works:
The script identifies:
Bullish FVGs when the current low is higher than the high of two bars ago.
Bearish FVGs when the current high is lower than the low of two bars ago.
Once an FVG is detected, it applies three optional independent filters:
GAP/ATR Filter:
Measures the FVG size relative to the Average True Range (ATR). Only gaps exceeding a user-defined multiple of ATR are considered valid.
Support/Resistance (S/R) Filter:
Uses pivot points to check if the FVG overlaps with recent high/low pivot levels within a tolerance percentage. This ensures the gap aligns with meaningful market levels.
Stochastic Filter:
Applies a stochastic oscillator to confirm momentum. Bullish FVGs are validated when stochastic values are oversold, and bearish FVGs when overbought.
After passing the selected filters, the strategy opens trades:
LONG FVG for bullish signals (buy)
SHORT FVG for bearish signals (sell)
The strategy automatically closes positions when an opposite signal appears, generating a backtest report with trades, profits, and statistics. The final bullish or bearish FVG signals are plotted as colored boxes on the chart with labels “BULL FVG” or “BEAR FVG” for immediate visual reference.
How to configure it for use:
Use GAP/ATR Filter: Enable or disable the ATR-based filter and adjust the ATR period (ATR Length) and minimum gap multiplier (Minimum Gap x ATR).
Use S/R Filter: Enable or disable the pivot-based S/R filter. Configure the pivot lookback periods (Pivot Left and Pivot Right) and the tolerance percentage (Gap Tolerance %).
Use Stochastic Filter: Enable or disable stochastic confirmation. Adjust the K and D lengths (Stoch K Length and Stoch D Length) and the overbought/oversold thresholds (Stoch Overbought and Stoch Oversold).
Colors: Customize the colors for bullish and bearish FVGs (FVG Bull and FVG Bear) to match your chart preferences.
Usage Tips:
Apply this strategy to any timeframe; shorter timeframes generate more frequent FVGs, while higher timeframes highlight stronger gaps.
Combine FVG signals with other technical analysis tools for better trade confirmation.
Use the box and label visualization to quickly scan charts for trade opportunities without cluttering the chart.
The strategy’s trades (LONG and SHORT) provide backtesting results and performance statistics for each signal.
Liquidity Sweep Breakout - LSBLiquidity Sweep Breakout - LSB
A professional session-based breakout system designed for OANDA:USDJPY and other JPY pairs.
Not guesswork, but precision - built on detailed observation of institutional moves to capture clear trade direction daily.
Master the Market’s Daily Bank Flow.
---
Strategy Detail:
I discovered this strategy after carefully studying how Japanese banks influence the forex market during their daily settlement period. Banks are some of the biggest players in the financial world, and when they adjust or settle their accounts in the morning, it often creates a push in the market. From years of observation, I noticed a consistent pattern, once banks finish their settlements, the market usually continues moving in the same direction that was formed right after those actions. This daily banking flow often sets the tone for the entire trading session, especially for JPY pairs like USDJPY.
To capture this move, I built the indicator so that it follows the bank-driven trend with clear rules for entries, stop-loss (SL), and take-profit (TP). The system is designed with professional risk management in mind. By default, it assumes a $10,000 account size, risks only 1% of that balance per trade, and targets a 1:1.5 reward-to-risk ratio. This means for every $100 risked, the potential profit is $150. Such controlled risk makes the system safer and more sustainable for long-term traders. At the same time, users are not limited to this setup, they can adjust the account balance in the settings, and the indicator will automatically recalculate the lot size and risk levels based on their own capital. This ensures the strategy works for small accounts and larger accounts alike.
🌍 Why It Works
Fundamentally driven: Based on **daily Japanese banking settlement flows**.
Session-specific precision: Targets the exact window when USDJPY liquidity reshapes.
Risk-managed: Always calculates lot size based on account and risk preferences.
Automatable: With webhook + MT5 EA, it can be fully hands-free.
---
✅ Recommended
Pair: USDJPY (best observed behavior).
Timeframe: 3-Minute chart.
Platform: TradingView Premium (for webhooks).
Execution: MT5 via EA.
---
🔎 Strategy Concept
The Tokyo Magic Breakout (TMB) is built on years of session observation and the unique daily rhythm of the Japanese banking system.
Every morning between 5:50 AM – 6:10 AM PKT (09:50 – 10:10 JST), Japanese banks perform daily reconciliation and settlement. This often sets the tone for the USDJPY direction of the day.
This strategy isolates that critical moment of liquidity adjustment and waits for a clean breakout confirmation. Instead of chasing noise, it executes only when price action is aligned with the Tokyo market’s hidden flows.
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🕒 Timing Logic
Session Start: 5:00 AM PKT (Tokyo market open range).
Magic Candle: The 5:54 AM PKT candle is marked as the reference “breakout selector.”
Checkpoints: First confirmation at 6:30 AM PKT, then every 15 minutes until 8:30 AM PKT.
* If price stays inside the magic range → wait.
* If a breakout happens but the candle wick touches the range → wait for the next checkpoint.
* If by 8:30 AM PKT no clean breakout occurs → the day is marked as No Trade Day (NTD).
👉 Recommended timeframe: 3-Minute chart (3M) for precise signals.
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📈 Trade Execution
Entry: Clean break above/below the magic candle’s range.
Stop-Loss: Opposite side of the Tokyo session high/low.
Take-Profit: Calculated by Reward\:Risk ratio (default 1.5:1).
Lot Size: Auto-calculated based on your risk model:
* Fixed Dollar
* % of Equity
* Conservative (minimum of both).
Visuals include:
✅ Entry/SL/TP lines
✅ Shaded risk (red) and reward (green) zones
✅ Trade labels (Buy/Sell with lot size & levels)
✅ TP/SL hit markers
---
🔔 Alerts & Automation (AutoTMB)
This strategy is fully automation-ready with EA + MT5:
1. Enable alerts in TMB settings.
2. Insert your PineConnector License Key.
3. Configure your risk management preferences.
4. Create a TradingView alert → in the message box simply type:
Pine Script®
{{alert_message}}
and set the EA webhook.
Now, every breakout trade (with exact entry, SL, TP, and lot size) is sent instantly.
👉 On your MT5:
* Install the EA.
* Use the same license key.
* Run it on a VPS or local MT5 terminal.
You now have a hands-free trading system: AutoTMB.
Fundamental Strategy - anuragmundraFundamental Score Based Backtest
This strategy combines fundamental analysis with automated backtesting to help identify long-term investment opportunities. Instead of relying only on price action or technical indicators, it evaluates the financial health of a company and generates simulated BUY/SELL signals accordingly.
🔑 Key Parameters Considered:
Price-to-Earnings (P/E Ratio): Ensures the stock is not overpriced.
Return on Equity (ROE): Indicates efficiency of management and business profitability.
Debt-to-Equity Ratio (D/E): Evaluates leverage and financial risk.
Revenue Growth (YoY): Shows business expansion and demand.
EPS Growth: Reflects consistent profit generation for shareholders.
Sales Growth: Confirms topline improvement.
Profit Growth: Measures bottom-line strength.
✅ Buy Condition
When the fundamental score ≥ 70/100, the strategy enters a long position.
Score is based on meeting/exceeding thresholds for P/E, ROE, Revenue Growth, EPS Growth, Sales Growth, Profit Growth, and Debt-to-Equity.
❌ Sell/Exit Condition
When the score falls below 70, the position is closed.
⚡ How to Use
Designed for medium to long-term investors who prefer fundamentally strong companies.
Can be run in the Strategy Tester to evaluate the historical performance of any stock.
Suitable as a stock-picking filter rather than a short-term trading system.
📊 Notes
Some ratios (like ROE) are based on annual values (FY), while others (EPS, Revenue, Net Income) use TTM for recency.
Not all symbols/exchanges provide full fundamental data. If data is missing, some metrics may show as N/A.
⚠️ Disclaimer: This is an educational tool for research and backtesting only. It is not financial advice. Always combine with your own due diligence before making investment decisions.
SulCryptoversity_4H_BuySell_CryptoIndicatorThis strategy is designed specifically for the 4-hour timeframe on trading charts. It works primarily for Bitcoin (BTC) but can also be applied to other high-market-cap cryptocurrencies such as Ethereum (ETH), Solana (SOL), Ripple (XRP), Sui (SUI), and even various other coins.
Please note that this is not financial advice—trading involves significant risk, and you should only proceed at your own discretion. We are not liable for any losses incurred from following these signals.
This strategy may be more effective in leverage trading to maximize gains, but leverage trading is highly risky and only recommended for highly skilled traders, as you could lose all your money. For regular purposes, use spot trading.
To use it effectively, focus on the "Buy" and "Sell" signals for your entry and exit points. While an "Exit Buy" signal may appear, rely solely on the main Buy and Sell indicators for decision-making.
-SulCryptoversity aka yo4Q
ETH/SOL 1D Dynamic Trend Core - STRATEGY v 45Overview
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. Its core philosophy is rooted in confluence, meaning it requires multiple conditions across trend, momentum, and volume to align before generating a signal. This approach aims to filter out market noise and provide a clearer view of the underlying trend.
The script includes a comprehensive backtesting engine for strategy optimization and a rich, intuitive visual interface for real-time analysis.
How It Works: Core Logic
The engine validates signals through several sequential layers:
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that dynamically determines the primary market direction (Bullish, Bearish, or Consolidation).
Momentum Confirmation: Signals are then qualified using a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum is firmly aligned with the established trend.
Advanced Filtering Suite: A suite of optional filters provides robust confirmation and allows for deep customization:
Volume & ADX: Confirms that trades are supported by sufficient market participation and trend strength.
Market Regime: Gauges broad market health (e.g., using TOTAL market cap) to avoid trading against the entire market.
Multi-Timeframe (MTF) Analysis: Aligns signals with the dominant trend on a higher timeframe (e.g., Weekly).
BTC Cycle Analysis: Positions trades within the context of historical Bitcoin cycles using models like the Halving Cycle or Mayer Multiple.
On-Chart Visuals & Features
The script provides full transparency into its logic with a powerful on-chart interface.
IMPORTANT: For the live visual elements to function correctly, you must enable "Recalculate on every tick" in the script's settings (Settings > Properties).
Power Core Gauge: Located at the bottom-center of the chart, this gauge is the heart of the system. It displays the number of filter conditions currently met (e.g., 5/6) and "powers up" by glowing brighter as more conditions align, indicating a fully confirmed signal is ready.
Live Conditions Panel: This panel in the bottom-right corner acts as a real-time pre-flight checklist. It shows the status (pass/fail) of every individual filter, so you know exactly why a signal is, or is not, being generated.
Energized Trendline: The primary SAMA trendline changes color and intensity based on the strength and direction of the trend, offering immediate visual context.
BTC Halving Cycle Visualizer: Provides a background color guide to the different phases of the Bitcoin halving cycle for macro context.
How to Use & Configure
Select Operation Mode:
Backtest Mode: Use this to test different settings on historical data and find optimal configurations for a specific asset and timeframe.
Alerts-Only Mode: Use this for live trading to generate alert signals without cluttering the chart with backtest data. (Contact publisher for access to this version)
Configure Your Filters:
Start with the default filter settings.
If a potential setup is missed, check the Live Conditions Panel to see which specific filter blocked the signal.
Enable, disable, or adjust filters in the script's settings to match your trading style and the asset's characteristics.
Manage Your Risk:
Go to the "Risk & Exit" settings to configure your Stop Loss and Take Profit parameters to match your personal risk tolerance.
Disclaimer: This script is for educational and informational purposes only. It is not financial advice. All trading involves risk, and past performance is not indicative of future results. Please conduct your own research and backtesting before making any trading decisions.
Martin Strategy - No Loss Exit v3Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0 Martin Strategy1.0
BTC Dynamic Trend Core Strategy v45// The Dynamic Trend Core is a sophisticated, multi-layer trading strategy that provides both a quantitative //
// backtesting engine and a rich, intuitive visual interface. It is designed to identify high-probability //
// trend-following opportunities by requiring a confluence of conditions to be met before a signal is considered //
// valid. //
// //
// The system's philosophy is rooted in confirmation, seeking to filter out market noise by ensuring that trend, //
// momentum, market sentiment, and volume are all in alignment. //
// //
// --- CORE LOGIC COMPONENTS --- //
// 1. **Primary Trend Analysis (SAMA):** The foundation is a self-adjusting moving average (SAMA) that //
// determines the underlying market trend (Bullish, Bearish, or Consolidation). //
// //
// 2. **Confirmation & Momentum:** Signals are confirmed with a blend of the Natural Market Slope and a Cyclic //
// RSI to ensure momentum aligns with the primary trend. //
// //
// 3. **Advanced Filtering Layers:** A suite of optional filters allows for robust customization: //
// - **Volume & ADX:** Ensure sufficient market participation and trend strength. //
// - **Market Regime:** Uses total crypto market cap to gauge broad market health. //
// - **Multi-Timeframe (MTF):** Aligns signals with the dominant weekly trend. //
// - **BTC Cycle Analysis:** Uses Halving or Mayer Multiple models to position trades within historical //
// macro cycles. //
// //
// --- VISUAL INTERFACE --- //
// The strategy's real power comes from its on-chart visual feedback system, which provides full transparency. //
// ****Note: for this to be enabled recalculate 'on every tick' needs to be enabled in the properties settings. //
// 1. **Power Core Gauge:** Located at the bottom-center, this gauge is the heart of the system. It displays the //
// number of active filter conditions that have been met (e.g., 5/6). It "powers up" as more conditions align,//
// glowing brightly when a signal is fully confirmed and ready. //
// //
// 2. **Live Conditions Panel:** In the bottom-right corner, this panel acts as a detailed pre-flight checklist. //
// It shows the real-time status of every single filter, helping you understand exactly why a trade is (or //
// is not) being triggered. //
// //
// 3. **Energized Trendline:** The main SAMA trendline changes color and brightness based on the strength and //
// direction of the trend, providing immediate visual context. //
// //
// 4. **Halving cycle visualisation:** Visual guide to halving phases //
// //
// --- HOW TO USE --- //
// 1. **Select Operation Mode:** Use "Backtest Mode" to test settings and "Alerts-Only Mode" for live signals. //
// //
// 2. **Configure Strategy:** Start with the default filters. If a potential trade setup is missed, check the //
// **Live Conditions Panel** to see exactly which filter blocked the signal. Adjust the filters to suit your //
// specific asset and timeframe. //
// //
// 3. **Manage Risk:** Adjust the Risk & Exit settings to match your personal risk tolerance. //
Gold Multi TP Strategy📘 Strategy Description: Gold Multi Take-Profit Strategy (XAUUSD)
This strategy is designed for Gold (XAUUSD) and works on any timeframe (recommended: 15-min or higher). It executes trades based on a simple EMA crossover logic with optional higher-timeframe and ATR-based filters to confirm trend direction and volatility.
🔑 Core Features
✅ Directional control: Trade only long, short, or both directions (Strategy Direction)
✅ Multi-level Take Profit: Scale out at up to 4 configurable profit targets
✅ Fixed Stop Loss: Set custom SL distance for risk control
✅ Position Sizing: Allocate different percentages to each TP level
✅ HTF Trend Filter (optional): Align trades with weekly candle trend
✅ ATR Filter (optional): Improve entries with volatility-based filter
⚙️ Inputs Explained
Input Name Function
Strategy Direction Choose to trade all, long, or short only
Length of Filter Length of the moving average used for HTF trend filter
Candle Time Reference candle timeframe in minutes (e.g., 1440 for daily)
Length of ATR Period for ATR calculation (volatility)
HTF Higher timeframe for filter (e.g., 1 week)
Filter Checkbox Enable/disable trend filter
Stop Loss Fixed SL distance in price units
Qty_percent1-3 % of position allocated to TP1–TP3 (rest goes to TP4)
Take profit1–4 TP levels (in price units) from entry price
🧠 Logic Overview
Entry triggered on EMA 20/50 crossover
Optional filter: entry allowed only if current price is above its HTF MA (bullish) or below (bearish)
Position is scaled out at up to 4 profit levels using different qty_percent
SL remains fixed throughout the trade
📊 Best Use
Intraday trading on XAUUSD, ideally during London/NY sessions
Trending or breakout conditions
Works best with additional confluence (price action, S/R, news)
Aether SignalAether Signal is a professional TradingView indicator engineered for advanced traders who demand precise analysis, smart money concepts, and robust risk management. It systematically incorporates institutional trading techniques, automated level detection, and multi-level profit-taking for exceptional trade execution.
Support & Resistance: Aether Signal automatically identifies key support and resistance levels using mathematically rigorous algorithms, ensuring that traders see the most significant price barriers for their entries and exits.
Smart Money Concepts: The indicator is grounded in institutional trading logic, analyzing market structure to pinpoint where large market participants are engaging. It leverages volume and price interaction at critical zones, similar to harmonic liquidity nodes in professional strategies.
Precise Entry Points: Entry signals are generated when strict confluence conditions are met, ensuring signals align with underlying market structure, high-volume footprints, and optimal momentum. Stops are logically placed just beyond the validated support or resistance—on the opposite side of the key zone.
Triple Take Profits: Aether Signal equips traders to maximize returns with three intelligently placed take profit levels (TP1, TP2, TP3), allowing for strategic scaling out and adaptive trade management.
Supply & Demand Zones: The indicator scans for market imbalances by identifying high-probability supply and demand areas driven by institutional activity and volume anomalies, guiding traders toward potent reversal or continuation setups.
Advanced Risk Management: Robust risk controls are integrated, including logical stop loss suggestions and trade selection filters, to minimize overtrading and enhance consistency.
Win Rate: The system claims a win rate of up to 96% under optimal settings and strict adherence to its entry criteria, setting a high benchmark for performance (note: actual results may vary depending on market conditions and trader discipline).
Aether Signal is tailored for traders seeking the edge of institutional-grade analytics—offering comprehensive structure analysis, actionable alerts, and performance-focused features that merge automation with trader control.
PEAD strategy█ OVERVIEW
This strategy trades the classic post-earnings announcement drift (PEAD).
It goes long only when the market gaps up after a positive EPS surprise.
█ LOGIC
1 — Earnings filter — EPS surprise > epsSprThresh %
2 — Gap filter — first regular 5-minute bar gaps ≥ gapThresh % above yesterday’s close
3 — Timing — only the first qualifying gap within one trading day of the earnings bar
4 — Momentum filter — last perfDays trading-day performance is positive
5 — Risk management
• Fixed stop-loss: stopPct % below entry
• Trailing exit: price < Daily EMA( emaLen )
█ INPUTS
• Gap up threshold (%) — 1 (gap size for entry)
• EPS surprise threshold (%) — 5 (min positive surprise)
• Past price performance — 20 (look-back bars for trend check)
• Fixed stop-loss (%) — 8 (hard stop distance)
• Daily EMA length — 30 (trailing exit length)
Note — Back-tests fill on the second 5-minute bar (Pine limitation).
Live trading: enable calc_on_every_tick=true for first-tick entries.
────────────────────────────────────────────
█ 概要（日本語）
本ストラテジーは決算後の PEAD を狙い、
EPS サプライズがプラス かつ 寄付きギャップアップ が発生した銘柄をスイングで買い持ちします。
█ ロジック
1 — 決算フィルター — EPS サプライズ > epsSprThresh %
2 — ギャップフィルター — レギュラー時間最初の 5 分足が前日終値＋ gapThresh %以上
3 — タイミング — 決算当日または翌営業日の最初のギャップのみエントリー
4 — モメンタムフィルター — 過去 perfDays 営業日の騰落率がプラス
5 — リスク管理
• 固定ストップ：エントリー − stopPct %
• 利確：終値が日足 EMA( emaLen ) を下抜け
█ 入力パラメータ
• Gap up threshold (%) — 1 (ギャップ条件)
• EPS surprise threshold (%) — 5 (EPS サプライズ最小値)
• Past price performance — 20 (パフォーマンス判定日数)
• Fixed stop-loss (%) — 8 (固定ストップ幅)
• Daily EMA length — 30 (利確用 EMA 期間)
注意 — Pine の仕様上、バックテストでは寄付き 5 分足の次バーで約定します。
実運用で寄付き成行に合わせたい場合は calc_on_every_tick=true を有効にしてください。
────
ご意見や質問があればお気軽にコメントください。
Happy trading!
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.
Sharpe Ratio Forced Selling StrategyThis study introduces the “Sharpe Ratio Forced Selling Strategy”, a quantitative trading model that dynamically manages positions based on the rolling Sharpe Ratio of an asset’s excess returns relative to the risk-free rate. The Sharpe Ratio, first introduced by Sharpe (1966), remains a cornerstone in risk-adjusted performance measurement, capturing the trade-off between return and volatility. In this strategy, entries are triggered when the Sharpe Ratio falls below a specified low threshold (indicating excessive pessimism), and exits occur either when the Sharpe Ratio surpasses a high threshold (indicating optimism or mean reversion) or when a maximum holding period is reached.
The underlying economic intuition stems from institutional behavior. Institutional investors, such as pension funds and mutual funds, are often subject to risk management mandates and performance benchmarking, requiring them to reduce exposure to assets that exhibit deteriorating risk-adjusted returns over rolling periods (Greenwood and Scharfstein, 2013). When risk-adjusted performance improves, institutions may rebalance or liquidate positions to meet regulatory requirements or internal mandates, a behavior that can be proxied effectively through a rising Sharpe Ratio.
By systematically monitoring the Sharpe Ratio, the strategy anticipates when “forced selling” pressure is likely to abate, allowing for opportunistic entries into assets priced below fundamental value. Exits are equally mechanized, either triggered by Sharpe Ratio improvements or by a strict time-based constraint, acknowledging that institutional rebalancing and window-dressing activities are often time-bound (Coval and Stafford, 2007).
The Sharpe Ratio is particularly suitable for this framework due to its ability to standardize excess returns per unit of risk, ensuring comparability across timeframes and asset classes (Sharpe, 1994). Furthermore, adjusting returns by a dynamically updating short-term risk-free rate (e.g., US 3-Month T-Bills from FRED) ensures that macroeconomic conditions, such as shifting interest rates, are accurately incorporated into the risk assessment.
While the Sharpe Ratio is an efficient and widely recognized measure, the strategy could be enhanced by incorporating alternative or complementary risk metrics:
• Sortino Ratio: Unlike the Sharpe Ratio, the Sortino Ratio penalizes only downside volatility (Sortino and van der Meer, 1991). This would refine entries and exits to distinguish between “good” and “bad” volatility.
• Maximum Drawdown Constraints: Integrating a moving window maximum drawdown filter could prevent entries during persistent downtrends not captured by volatility alone.
• Conditional Value at Risk (CVaR): A measure of expected shortfall beyond the Value at Risk, CVaR could further constrain entry conditions by accounting for tail risk in extreme environments (Rockafellar and Uryasev, 2000).
• Dynamic Thresholds: Instead of static Sharpe thresholds, one could implement dynamic bands based on the historical distribution of the Sharpe Ratio, adjusting for volatility clustering effects (Cont, 2001).
Each of these risk parameters could be incorporated into the current script as additional input controls, further tailoring the model to different market regimes or investor risk appetites.
References
• Cont, R. (2001) ‘Empirical properties of asset returns: stylized facts and statistical issues’, Quantitative Finance, 1(2), pp. 223-236.
• Coval, J.D. and Stafford, E. (2007) ‘Asset Fire Sales (and Purchases) in Equity Markets’, Journal of Financial Economics, 86(2), pp. 479-512.
• Greenwood, R. and Scharfstein, D. (2013) ‘The Growth of Finance’, Journal of Economic Perspectives, 27(2), pp. 3-28.
• Rockafellar, R.T. and Uryasev, S. (2000) ‘Optimization of Conditional Value-at-Risk’, Journal of Risk, 2(3), pp. 21-41.
• Sharpe, W.F. (1966) ‘Mutual Fund Performance’, Journal of Business, 39(1), pp. 119-138.
• Sharpe, W.F. (1994) ‘The Sharpe Ratio’, Journal of Portfolio Management, 21(1), pp. 49-58.
• Sortino, F.A. and van der Meer, R. (1991) ‘Downside Risk’, Journal of Portfolio Management, 17(4), pp. 27-31.
Supertrend + MACD CrossoverKey Elements of the Template:
Supertrend Settings:
supertrendFactor: Adjustable to control the sensitivity of the Supertrend.
supertrendATRLength: ATR length used for Supertrend calculation.
MACD Settings:
macdFastLength, macdSlowLength, macdSignalSmoothing: These settings allow you to fine-tune the MACD for better results.
Risk Management:
Stop-Loss: The stop-loss is based on the ATR (Average True Range), a volatility-based indicator.
Take-Profit: The take-profit is based on the risk-reward ratio (set to 3x by default).
Both stop-loss and take-profit are dynamic, based on ATR, which adjusts according to market volatility.
Buy and Sell Signals:
Buy Signal: Supertrend is bullish, and MACD line crosses above the Signal line.
Sell Signal: Supertrend is bearish, and MACD line crosses below the Signal line.
Visual Elements:
The Supertrend line is plotted in green (bullish) and red (bearish).
Buy and Sell signals are shown with green and red triangles on the chart.
Next Steps for Optimization:
Backtesting:
Run backtests on BTC in the 5-minute timeframe and adjust parameters (Supertrend factor, MACD settings, risk-reward ratio) to find the optimal configuration for the 60% win ratio.
Fine-Tuning Parameters:
Adjust supertrendFactor and macdFastLength to find more optimal values based on BTC's market behavior.
Tweak the risk-reward ratio to maximize profitability while maintaining a good win ratio.
Evaluate Market Conditions:
The performance of the strategy can vary based on market volatility. It may be helpful to evaluate performance in different market conditions or pair it with a filter like RSI or volume.
Let me know if you'd like further tweaks or explanations!
Earnings Trading StrategyThe Earnings Trade Strategy automates the process of entering and exiting trades based on earnings announcements for Apple (AAPL). It allows users to take a position—either long (buy) or short (sell short)—on the trading day before an earnings announcement and close that position on the trading day after the announcement. By leveraging TradingView’s Paper Trading environment, the strategy enables users to simulate trades and collect performance data over a 6-month period in a risk-free setting.
