Alpha Research5
Component Architecture
Branch: rikk_mtf_backtest001 | Companion to: Alpha_Research
Component-level implementation of the MTFDR backtesting system. Each component translates trading philosophy into executable Python.
PART 1: Pipeline Architecture
The system processes each 5-minute bar through 5 sequential layers:
Raw OHLC Data (1D, 4H, 1H, 15M, 5M)
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ 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) │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ 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%) │
│ • Risk/Reward (15%) │
│ │
│ Output: SetupQualityResult (grade, score, position_size) │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ 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 │
│ │
│ Gate Filters: Hour, MA Direction, Probability Zone │
│ Output: Signal (direction, entry, stop, target, grade) │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────────────┘
PART 2: Component Index
| # | Component | Purpose | Code Link |
|---|---|---|---|
| 1 | multi_timeframe_analysis.py | MTF alignment across 5 timeframes | View |
| 2 | trend_analysis_core.py | Per-timeframe trend direction, MA slopes | View |
| 3 | market_state_analysis.py | Railroad, creeper, volatility, trend phase | View |
| 4 | setup_quality_detection.py | 5-factor scoring, A+ to F grades | View |
| 5 | signal_generation_trade_management.py | Direction, filters, entry/stop/target | View |
| 6 | trade_execution_engine.py | Execution, position management, P&L | View |
| 7 | probability_zone_analysis.py | Probability zones (80%/65%/35%/15%), crash bar | View |
| 8 | data_manager.py | Data loading, timeframe aggregation | View |
| 9 | backtesting_analytics.py | Performance metrics, reporting | View |
PART 3: Trading Concept → Code Mapping
| Trading Concept | Status | Component | Implementation |
|---|---|---|---|
| Halves/Thirds | ✅ | probability_zone_analysis.py | Top third = 80%, Bottom third = 15% continuation |
| Crash Bar | ✅ | probability_zone_analysis.py | Bar 2x average = structural break |
| Three-Finger Spread | ✅ | probability_zone_analysis.py | Price/21MA/200MA separation detection |
| 45-degree Angle | ✅ | trend_analysis_core.py | MA slope analysis, flattish detection |
| Railroad Trend | ✅ | market_state_analysis.py | Consistency > 80%, 3+ strong bars |
| Creeper Move | ✅ | market_state_analysis.py | Avg range < 0.5% over 7 bars |
| Two-Day Trend | ✅ | market_state_analysis.py | Both daily bars close same direction |
| Color Change | ✅ | probability_zone_analysis.py | Liquidity sweep pattern detection |
| Fab Four | ❌ | - | Not implemented (Three-Finger Spread is closest) |
| Traffic Jam | ❌ | - | Not implemented |
PART 4: Key Configuration Parameters
Setup Quality Weights
timeframe_alignment_weight = 0.30 # 30%
trend_strength_weight = 0.20 # 20%
entry_technique_weight = 0.15 # 15%
key_level_proximity_weight = 0.20 # 20%
risk_reward_weight = 0.15 # 15%
-------
1.00
Penalty & Bonus Constants
creeper_move_penalty = -50 ma_struggle_penalty = -30 two_day_trend_penalty = -30 phase_mismatch_penalty = -25 railroad_trend_bonus = +15 key_level_bonus = +10 clean_entry_bonus = +10
Grade Thresholds & Position Sizing
A+ = score >= 90 → 2 lots A = score >= 80 → 1 lot B = score >= 70 → 1 lot C = score >= 60 → 1 lot D = score >= 50 → 1 lot F = score < 50 → 1 lot
Trading Constants
commission_per_lot = 20.00 # Rs per lot (Dhan) transaction_tax_rate = 0.0001 # 0.01% CTT lot_size_multiplier = 100 # MCX lot size min_stop_distance = 40 # Points default_risk_reward = 1.5 # R:R ratio