Hash Momentum Strategy

1. Executive Summary

The Hash Momentum Strategy is a systematic momentum-acceleration framework implemented in Pine Script (v6) for TradingView under the name “Hash Momentum Strategy” (publication link: https://www.tradingview.com/script/L6VNlhiV-Hash-Momentum-Strategy/).

Hash Momentum Strategy

Published on Community Indicators TradingView

The design objective is to capture early-stage directional moves with pre-defined risk, using:

• Momentum acceleration rather than simple EMA crossovers

• Programmable risk‑reward with fixed percentage stop and configurable R:R

• Partial profit taking in multiple stages to derisk winners while keeping upside open

• Volatility-aware filters via ATR-based momentum thresholds

• Execution discipline controls such as trade cooldowns, session filters, and weekend filters

The strategy is intended as a rule-based execution engine for discretionary or systematic operators seeking consistent momentum exposure with institutional risk framing. It is not a prediction engine and does not override portfolio-level risk management.

For educational discussion only. Not financial advice.

2. Strategy Overview

2.1 Core Concept

The Hash Momentum Strategy is a trend-following, momentum-acceleration system with the following core characteristics:

• Direction: Trades in the direction of prevailing trend, confirmed by EMA

• Signal Driver: Short-to-medium-term price momentum and its acceleration

• Volatility Filter: Uses ATR to define a dynamic minimum momentum threshold

• Execution Style: Single-position, non-pyramiding, percentage-of-equity sizing

• Timeframes: Optimized for 1H baseline; configurable for 15M and 4H

The system aims to enter as momentum is building, rather than after a full trend has already developed, targeting a higher average R:R per trade at the cost of a moderate win rate.

3. Signal Generation & Trade Logic

3.1 Momentum Engine

• Momentum is defined as the price change between current close and close N bars ago.

• The system requires:

  • Absolute momentum to exceed an ATR-based dynamic threshold (ATR × momentum threshold multiplier)

  • Acceleration in momentum (momentum change in the current direction)

  • Normalized momentum (momentum expressed in σ units) to exceed a minimum strength filter

Intuition: Only engage when price is moving strongly and increasingly in one direction, relative to recent volatility. This reduces noise in range-bound regimes.

3.2 Trend Filter (EMA)

• An EMA trend filter is used as a directional gate:

  • Longs only when price is above EMA

  • Shorts only when price is below EMA

• EMA length is configurable (default: 28 on 1H), functioning as a regime filter to stay aligned with the dominant swing.

3.3 Environmental Filters

To control trade quality and structural risk, the strategy applies:

• Session Filters (optional)

  • Tokyo, London, New York sessions can be individually toggled

  • Default: OFF, but recommended to focus on London/NY for higher liquidity pairs

• Weekend Filter (optional)

  • “Weekend Off” disables trading on Saturday and Sunday to avoid structurally thin liquidity conditions (especially relevant for FX/indices; optional for crypto).

3.4 Cooldown Logic

• After closing a trade, the strategy can enforce a cooldown (in bars) before allowing a new entry.

• Default: 6 bars (1H baseline), configurable between 1 and 24.

• Purpose:

  • Prevent reactive “chasing” after a win or loss

  • Reduce clustering of trades in choppy conditions

  • Encourage only distinct momentum bursts to be traded

4. Risk Management & Trade Construction

4.1 Stop Loss Construction

• Stop Loss % is defined as a fixed percentage from entry (default: 2.2%).

• For longs: stop = entry × (1 – SL%)

• For shorts: stop = entry × (1 + SL%)

This provides:

• Clear ex-ante risk per trade

• Easy alignment with portfolio-level risk parameters (e.g., 1–2% of equity per trade, per the operator’s internal risk framework).

4.2 Take Profit & Risk–Reward

• Primary parameter: Risk:Reward Ratio (default: 2.5:1).

• The system computes the full-target distance as a multiple of the defined risk per trade (distance from entry to stop).

4.3 Partial Profit Taking Logic

When enabled (default: ON), the system splits exits into three stages:

1. TP1 – Early Realization

   • Default: 2.0R target

   • Default size: 50% of position

   • Objective: lock in gains and reduce downside variance.

2. TP2 – Core Realization

   • Default: 2.5R target

   • Default size: 40% of position

   • Objective: crystallize a substantial portion of the move.

3. Final TP – Extended Move

   • R:R defined by main Risk:Reward Ratio (default: 2.5 but configurable higher)

   • Residual 10% position left to capture extended runs.

The partial TP structure converts the strategy from an “all‑or‑nothing” profile to a laddered realization profile, with:

• Lower psychological stress

• Smoother equity curve

• Retained convexity in extended trends.

Partial TPs can be fully disabled for desks that prefer single-target exits.

5. Recommended Configurations

5.1 1-Hour Baseline (Default)

• Momentum Length: 13

• Momentum Threshold (ATR Multiplier): 2.25

• EMA Length: 28

• Stop Loss: 2.2%

• R:R Ratio: 2.5

• Cooldown: 6 bars

Optimized for liquid majors (crypto, FX, indices) with a balance between trade frequency and signal quality.

5.2 Higher Timeframe Swing (4H)

• Momentum Length: 24–36

• Momentum Threshold: 2.5

• EMA Length: 50

• Stop Loss: 3–4%

• R:R Ratio: 2.0–2.5

• Cooldown: 6–8 bars

Use case: swing exposure in trending environments; fewer but higher-conviction trades.

5.3 Lower Timeframe Execution (15M)

• Momentum Length: 8–10

• Momentum Threshold: 2.0

• EMA Length: 20

• Stop Loss: 1.5–2%

• R:R Ratio: 2.0

• Cooldown: 4–6 bars

Use case: intraday execution aligned with higher timeframe directional bias (e.g., 4H/1H).

6. Performance Expectations (Indicative)

Based on typical backtests (parameters as above, on liquid instruments):

• Indicative Win Rate: ~35–45%

• Profit Factor: ~1.5–2.0

• Target Risk:Reward: ~1:2.5 per trade

• Max Drawdown Range: ~10–20% (parameter-dependent)

• Activity: ~8–15 trades/month on 1H

Key observation:The design assumes a modest win rate but elevated R:R, meaning the edge comes from asymmetric payoffs, not frequent wins. With 2.5:1 R:R, the theoretical break-even win rate is approximately 29%.

All figures are illustrative and asset/timeframe dependent. They are not guarantees and should be validated through instrument- and venue-specific testing.

4HR Bitcoin Chart

7. Implementation Details

7.1 Platform & Parameters

• Implemented as a TradingView strategy (not just an indicator) with:

  • strategy.percent_of_equity sizing (default 100% for backtesting clarity)

  • Commission and slippage parameters configurable (default includes 0.075% commission and 5 ticks slippage).

• Operators should align:

  • Commission/slippage settings with actual venue costs

  • Position sizing (e.g., 1–2% equity risk) via portfolio-level risk models, not solely via strategy defaults.

7.2 Visual & Monitoring Features

• EMA line colored by trend (bullish/bearish)

• Signal markers:

  • Green dot (below bar): Long entry

  • Red dot (above bar): Short entry

  • Blue X: Long exit

  • Orange X: Short exit

• Lines:

  • White: Entry

  • Red: Stop Loss

  • Green: Take Profit levels (TP1, TP2, final)

7.3 Dashboard (Backtest Mode)

A compact dashboard (hidden in real-time to keep charts clean) displays:

• Position state (LONG/SHORT/FLAT)

• Entry, stop, and primary TP levels

• Configured R:R

• Normalized momentum strength (σ)

• Aggregate trade count, win rate, and net profit % vs initial capital

This allows a high-level review of current state and historical performance without leaving the chart.

7.4 Alerts

The strategy defines alerts for:

• Long signal

• Short signal

• Position opened

• Position closed

These can be integrated with execution bridges or notification workflows, subject to the desk’s infrastructure and controls.

8. Use Cases & Operator Playbook

8.1 Primary Use Cases

• Systematic Momentum Sleeve

  • Component of a diversified systematic portfolio for momentum exposure.

• Discretionary Confirmation Tool

  • Used by discretionary traders to time entries within a known macro/structural bias.

• Execution Engine Across Timeframes

  • 4H/1H for directional bias

  • 1H/15M for actual order-level execution.

8.2 Parameter Tuning Guidelines

To increase trade frequency:

• Lower momentum threshold (ATR multiplier)

• Shorten momentum length

• Reduce cooldown bars

To increase trade quality / reduce noise:

• Increase momentum threshold

• Lengthen momentum lookback

• Increase cooldown bars

• Tighten session filters (e.g., London/NY only)

To reduce drawdown:

• Increase cooldown

• Use tighter stop loss (with awareness of potential noise)

• Avoid weekends where appropriate

• Focus on high-liquidity sessions/instruments.

9. Key Risks & Limitations

1. Regime Dependence

   • Performance is generally stronger in trending, volatile markets.

   • Range-bound, low-vol regimes can lead to sequences of small losses or whipsaws.

2. Execution Frictions

   • Actual performance depends on:

     • Venue liquidity

     • Slippage and spread

     • Latency and order routing

   • Commission and slippage assumptions in backtests must be conservative and realistic.

3. News & Event Risk

   • High-impact macro or idiosyncratic events can cause gaps and invalidation of usual price behavior.

   • Desk-level SOP should consider pausing strategy around major scheduled news.

4. Model Risk / Overfitting

   • While the design avoids exotic parameters, any fixed rule set is exposed to structural market shifts.

   • Robustness checks across instruments, regimes, and timeframes are required.

5. Leverage & Position Sizing

   • The strategy itself does not enforce leverage caps beyond Pine settings.

   • Over-leveraging materially increases drawdown and tail risk.

10. Operational Recommendations

• Backtest Per Instrument & Venue

  • Run extensive walk-forward and out-of-sample tests with realistic assumptions.

• Start in Paper / Demo Environments

  • Validate behavior under live data before enabling real capital.

• Define Portfolio-Level Risk Rules

  • Max risk per trade, per day, per strategy.

  • Correlation controls if running multiple strategies.

• Implement News & Downtime SOPs

  • Rules for pausing during extreme volatility, platform maintenance, or structural dislocations.

• Monitor & Review

  • Periodic review of:

    • Win rate vs expectation

    • Drawdown profile

    • Trade distribution by session and instrument

  • Adjust parameters, not core logic, unless clear structural failures are observed.

11. Conclusion

The Hash Momentum Strategy provides a transparent, rules-based momentum framework that emphasizes:

• Early entry into accelerating moves

• Clear, programmable risk-reward

• Systematic partial profit taking

• Execution discipline via filters and cooldowns

It is well-suited as a component of a risk-aware momentum sleeve and as a structured execution tool for professional operators. Proper due diligence, conservative sizing, and strict operational controls remain essential.

For educational discussion only. Not financial advice.