BacktestIQ Architecture
BacktestIQ – Strategy Simulation Platform Component Architecture
[edit]PART 1: Pipeline Architecture
[edit]Pre-aggregated OHLC data (5M, 15M, 1H, 4H, 1D) derived from 1-minute source. At present conversion doesnot happen at runtime, it is done beforehand. data_manager.py just loads pre-converted CSV files.
Raw OHLC Data (1D, 4H, 1H, 15M, 5M)
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│ LAYER 1: multi_timeframe_analysis.py │
│ "Are all timeframes pointing the same direction?" │
│ │
│ • Analyzes [1D, 4H, 1H, 15M, 5M] via TrendAnalysisCore │
│ • Calculates weighted alignment score (hierarchical weights) │
│ • 15-minute confirmation requirement │
│ • Output: TimeframeAnalysisResult (aligned, direction, score) │
└─────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────┐
│ LAYER 2: market_state_analysis.py │
│ "What kind of market are we in?" │
│ │
│ 7 Detection Algorithms: │
│ 1. Railroad Trend - Strong one-sided move (consistency > 80%) │
│ 2. Creeper Move - Slow grinding action (avg range < 0.5%) │
│ 3. Volatility Classification - High/Normal/Low │
│ 4. Market State - Trending/Range/Creeper/Momentum │
│ 5. Two-Day Trend - Both days close same direction │
│ 6. Trend Phase - EARLY/MIDDLE/LATE │
│ 7. Institutional Behavior - Fight, Accumulation, BOS │
│ Output: MarketStateResult │
└─────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────┐
│ LAYER 3: setup_quality_detection.py │
│ "How good is this setup? Grade it A+ to F." │
│ │
│ 5-Factor Weighted Scoring: │
│ • Timeframe Alignment (30%) │
│ • Trend Strength (20%) │
│ • Entry Quality (15%) │
│ • Key Level Proximity (20%) [WIP JIRA] │
│ • Risk/Reward (15%) │
│ │
│ Output: SetupQualityResult (grade, score, position_size) │
└─────────────────────────────────────────────────────────────────────┘
│
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┌─────────────────────────────────────────────────────────────────────┐
│ LAYER 4: signal_generation_trade_management.py │
│ "Should we trade? What direction? Where's entry/stop/target?" │
│ │
│ Direction Logic: │
│ • MA21 rising → LONG │
│ • MA21 declining → SHORT │
│ • Flat → MTF fallback [WIP market consolidation] │
│ │
│ Gate Filters: Hour, MA Direction, Probability Zone [WIP JIRA] │
│ Output: Signal (direction, entry, stop, target, grade) │
└─────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────┐
│ LAYER 5: trade_execution_engine.py │
│ "Execute the trade, manage the position, calculate P&L" │
│ │
│ Execution: Slippage modeling, Commission (Rs 20/lot), CTT (0.01%) │
│ Position Management: Trailing stop, Breakeven stop, Time exit │
│ Output: Trade objects with complete P&L [WIP JIRA] │
└─────────────────────────────────────────────────────────────────────┘
PART 2: Component Index
[edit]| # | Component | Purpose | Owner |
|---|---|---|---|
| 1 | multi_timeframe_analysis.py | MTF alignment across 5 timeframes | Pawan |
| 2 | trend_analysis_core.py | Per-timeframe trend direction, MA slopes | Chayan |
| 3 | market_state_analysis.py | Railroad, creeper, volatility, trend phase | Vishal |
| 4 | setup_quality_detection.py | 5-factor scoring, A+ to F grades | Vishal |
| 5 | signal_generation_trade_management.py | Direction, filters, entry/stop/target | Chayan |
| 6 | trade_execution_engine.py | Execution, position management, P&L | Harshad |
| 7 | probability_zone_analysis.py | Probability zones, crash bar [Work in progress] | Rikk |
| 8 | data_manager.py | Data loading, timeframe aggregation | Harshad |
| 9 | backtesting_analytics.py | Performance metrics, reporting | Pawan |