ALGORITHMIC TRADING SYSTEMS

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1

Trend Structure Classification

The algorithm applies exponential moving average convergence analysis across three timeframes (9, 21, 50 periods) to identify market regime. Trend direction is classified using statistical distance from key EMAs, creating a hierarchical filter that eliminates counter-trend noise.

2

Momentum Oscillator Confluence

RSI, MACD histogram divergence, and volume-weighted momentum indicators must achieve simultaneous alignment. The system requires RSI above 50 for long bias, MACD histogram positive slope, and volume confirmation exceeding the 20-period moving average—all concurrent with price action.

3

Probabilistic Scoring Engine

Each technical factor receives a weighted score based on historical win rate contribution. The aggregate score ranges from 0-100, representing setup quality. Only configurations scoring above 90 trigger entry signals. This threshold was determined through Monte Carlo simulation across 1,000+ parameter combinations to maximize risk-adjusted returns.

4

ATR-Based Dynamic Risk Allocation

Stop-loss placement utilizes Average True Range (14-period) to adapt to current volatility conditions. The system sets stops at 2.9% of entry price, approximately 1 ATR distance, ensuring stops are wide enough to avoid noise but tight enough to preserve capital. Take-profit targets operate on a 1:1.5 and 1:3 risk-reward ratio, with 50% position closed at TP1 and remaining at TP2.

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PERFORMANCE

Test Period: Jan 2023 → Jan 2026
Initial Capital: $1,000
Current Value: $1,730.73
Total Return: +73.07%
Annual Return: ~24.36%

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STATISTICS

Win Rate: 63.2% (12/19 signals)
Profit Factor: 4.06
Avg Win: +8.48%
Avg Loss: -3.08%
Risk/Reward: 1:2.75

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SYSTEM DETAILS

Asset: SPY (S&P 500 ETF)
Timeframe: 1 Hour
Stop Loss: 2.9% (1 ATR)
Signal Frequency: ~6/year
Type: Algorithmic scoring

19

Total Signals

12

Winning Signals

7

Losing Signals

+73%

Total Return