Scikit-learn
English
trading
finance
gold
xauusd
forex
algorithmic-trading
smart-money-concepts
smc
xgboost
lightgbm
machine-learning
backtesting
technical-analysis
multi-timeframe
intraday-trading
high-frequency-trading
ensemble-model
capital-preservation
risk-management
recovery-mechanisms
Eval Results
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README.md
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| 1 |
+
---
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| 2 |
+
language: en
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| 3 |
+
license: mit
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| 4 |
+
library_name: sklearn
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| 5 |
+
tags:
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| 6 |
+
- trading
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| 7 |
+
- finance
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| 8 |
+
- gold
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| 9 |
+
- xauusd
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| 10 |
+
- forex
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| 11 |
+
- algorithmic-trading
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| 12 |
+
- smart-money-concepts
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| 13 |
+
- smc
|
| 14 |
+
- xgboost
|
| 15 |
+
- lightgbm
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| 16 |
+
- machine-learning
|
| 17 |
+
- backtesting
|
| 18 |
+
- technical-analysis
|
| 19 |
+
- multi-timeframe
|
| 20 |
+
- intraday-trading
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| 21 |
+
- high-frequency-trading
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| 22 |
+
- ensemble-model
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| 23 |
+
- capital-preservation
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| 24 |
+
- risk-management
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| 25 |
+
- recovery-mechanisms
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| 26 |
+
datasets:
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| 27 |
+
- yahoo-finance-gc-f
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| 28 |
+
metrics:
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| 29 |
+
- accuracy
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| 30 |
+
- precision
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| 31 |
+
- recall
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| 32 |
+
- f1
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| 33 |
+
- sharpe
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| 34 |
+
- max_drawdown
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| 35 |
+
- cagr
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| 36 |
+
- win_rate
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| 37 |
+
- profit_factor
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| 38 |
+
- capital_preservation_score
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| 39 |
+
model-index:
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| 40 |
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- name: romeo-v7-15m
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| 41 |
+
results:
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| 42 |
+
- task:
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| 43 |
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type: binary-classification
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| 44 |
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name: 15-Minute Price Direction Prediction with Capital Preservation
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| 45 |
+
dataset:
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| 46 |
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type: yahoo-finance-gc-f
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| 47 |
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name: Gold Futures (GC=F)
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| 48 |
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metrics:
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| 49 |
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- type: accuracy
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| 50 |
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value: 57.1
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| 51 |
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name: Win Rate
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| 52 |
+
- type: profit_factor
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| 53 |
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value: 2.10
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| 54 |
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name: Profit Factor
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| 55 |
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- type: max_drawdown
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| 56 |
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value: 8.2
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| 57 |
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name: Max Drawdown
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| 58 |
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- type: capital_preservation_score
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| 59 |
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value: 28.4
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| 60 |
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name: Capital Preservation Score
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| 61 |
+
---
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| 62 |
+
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| 63 |
+
# Romeo V7 — Capital Preservation & Recovery Trading Model
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| 64 |
+
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| 65 |
+
## Model Details
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| 66 |
+
|
| 67 |
+
### Model Description
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| 68 |
+
Romeo V7 is an enhanced version of Romeo V6 with advanced capital preservation strategies, recovery mechanisms, and consistent profitability features. It combines tree-based models (XGBoost and LightGBM) with sophisticated risk management to provide stable returns with lower drawdown.
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| 69 |
+
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| 70 |
+
- **Model Type**: Ensemble Classifier with Capital Preservation (XGBoost + LightGBM)
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| 71 |
+
- **Asset**: XAUUSD (Gold Futures)
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| 72 |
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- **Strategy**: Smart Money Concepts (SMC) with capital preservation and recovery
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| 73 |
+
- **Prediction Horizon**: 15-minute intraday (next bar direction)
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| 74 |
+
- **Framework**: Scikit-learn, XGBoost, LightGBM
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| 75 |
+
|
| 76 |
+
### Key Enhancements over V6
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| 77 |
+
- **Dynamic Position Sizing**: Adjusts position sizes based on current capital and drawdown
|
| 78 |
+
- **Recovery Mechanisms**: Reduces risk during drawdown periods, increases confidence during profitable periods
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| 79 |
+
- **Confidence-Based Filtering**: Only trades high-confidence signals with volume and volatility confirmation
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| 80 |
+
- **Capital Preservation Rules**: Multiple safety checks to protect capital during adverse conditions
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| 81 |
+
- **Volatility Adjustment**: Reduces position sizes during high volatility periods
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| 82 |
+
|
| 83 |
+
### Model Architecture
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| 84 |
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- **Ensemble Components**:
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| 85 |
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- XGBoost Classifier: Gradient boosting with conservative parameters
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| 86 |
+
- LightGBM Classifier: Efficient gradient boosting with risk-aware features
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| 87 |
+
- **Enhanced Features**: 52 features including capital preservation indicators, recovery signals, and risk metrics
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| 88 |
+
- **Capital Preservation Engine**: Dynamic position sizing, confidence filtering, recovery mode logic
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| 89 |
+
- **Serialization**: Tree models saved in joblib format
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| 90 |
+
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| 91 |
+
### Intended Use
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| 92 |
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- **Primary Use**: Research, backtesting, and evaluation on historical XAUUSD data with capital preservation
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| 93 |
+
- **Secondary Use**: Educational purposes for understanding risk-managed trading models
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| 94 |
+
- **Out-of-Scope**: Not financial advice. Requires proper validation and risk controls for live trading
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| 95 |
+
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| 96 |
+
### Factors
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| 97 |
+
- **Relevant Factors**: Market volatility, economic indicators, capital preservation requirements
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| 98 |
+
- **Evaluation Factors**: Tested on unseen data with realistic slippage, commission, and risk management
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| 99 |
+
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| 100 |
+
### Metrics (Capital Preservation Mode)
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| 101 |
+
- **Evaluation Data**: Unseen 15m intraday data (out-of-sample)
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| 102 |
+
- **Risk Parameters**: 10% risk per trade, 2% stop loss, 5% take profit
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| 103 |
+
- **Capital Preservation Settings**: 65% confidence threshold, dynamic sizing enabled
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| 104 |
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- **Metrics**:
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| 105 |
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- Initial Capital: 100
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| 106 |
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- Final Capital: 144.24
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| 107 |
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- Total Return: 44.24%
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| 108 |
+
- Max Drawdown: 8.2%
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| 109 |
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- Total Trades: 133
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| 110 |
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- Win Rate: 57.1%
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| 111 |
+
- Profit Factor: 2.10
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| 112 |
+
- Sharpe Ratio: 4.37
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| 113 |
+
- Capital Preservation Score: 28.4/100
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| 114 |
+
- Recovery Effectiveness: 100%
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| 115 |
+
- Risk-Adjusted Return: 5.38
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| 116 |
+
- High Confidence Trades: 98/133 (74%)
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| 117 |
+
- Recovery Mode Trades: 0/133 (0%)
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| 118 |
+
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| 119 |
+
### Capital Preservation Features
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| 120 |
+
- **Dynamic Position Sizing**: Adjusts based on capital, drawdown, and volatility
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| 121 |
+
- **Recovery Mode**: Activates when drawdown exceeds 85%, reduces risk by 50%
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| 122 |
+
- **Confidence Filtering**: Minimum 65% confidence required for trades
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| 123 |
+
- **Volatility Control**: Reduces position sizes during high volatility (>1.5% ATR)
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| 124 |
+
- **Volume Confirmation**: Requires volume above 20-period average for entry
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| 125 |
+
- **Safe Zone Trading**: Prefers entries within support/resistance levels
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| 126 |
+
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| 127 |
+
### Usage Instructions
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| 128 |
+
```python
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| 129 |
+
from v7.backtest_v7 import CapitalPreservationBacktester
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| 130 |
+
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| 131 |
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# Initialize with capital preservation settings
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| 132 |
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backtester = CapitalPreservationBacktester({
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| 133 |
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'confidence_threshold': 0.65,
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| 134 |
+
'max_risk_per_trade': 0.15,
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| 135 |
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'recovery_mode_threshold': 0.85,
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| 136 |
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'volatility_adjustment': True,
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| 137 |
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'dynamic_position_sizing': True
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| 138 |
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})
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| 139 |
+
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| 140 |
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# Run backtest
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| 141 |
+
results = backtester.backtest_capital_preservation(
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| 142 |
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risk_per_trade=0.10,
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| 143 |
+
stop_loss=0.02,
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| 144 |
+
take_profit=0.05
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| 145 |
+
)
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| 146 |
+
```
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| 147 |
+
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| 148 |
+
### Risk Management
|
| 149 |
+
- **Maximum Risk per Trade**: 15% of current capital
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| 150 |
+
- **Recovery Mode Threshold**: 85% drawdown triggers reduced risk
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| 151 |
+
- **Stop Trading Threshold**: 95% drawdown stops all trading
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| 152 |
+
- **Profit Target Reset**: Returns to normal risk after 2% profit recovery
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| 153 |
+
- **Volatility Filter**: Skips trades when volatility > 2%
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| 154 |
+
|
| 155 |
+
### Performance Comparison vs V6
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| 156 |
+
| Metric | Romeo V6 | Romeo V7 | Improvement |
|
| 157 |
+
|--------|----------|----------|-------------|
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| 158 |
+
| Total Return | 10.79% | 44.24% | +33.45% |
|
| 159 |
+
| Max Drawdown | Higher | 8.2% | Lower |
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| 160 |
+
| Win Rate | 49.28% | 57.1% | +7.82% |
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| 161 |
+
| Profit Factor | ~1.5 | 2.10 | +0.6 |
|
| 162 |
+
| Sharpe Ratio | N/A | 4.37 | N/A |
|
| 163 |
+
| Capital Preservation | Basic | Advanced | Major |
|
| 164 |
+
|
| 165 |
+
### Training Data
|
| 166 |
+
- **Source**: Yahoo Finance GC=F (Gold Futures)
|
| 167 |
+
- **Timeframe**: 15-minute intraday data
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| 168 |
+
- **Period**: Historical data with enhanced feature engineering
|
| 169 |
+
- **Augmentation**: Noise injection for robustness
|
| 170 |
+
- **Validation**: Out-of-sample testing with capital preservation metrics
|
| 171 |
+
|
| 172 |
+
### Ethical Considerations
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| 173 |
+
- Designed for capital preservation and risk management
|
| 174 |
+
- Includes multiple safety mechanisms to prevent excessive losses
|
| 175 |
+
- Recovery mechanisms help maintain trading capital during adverse conditions
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| 176 |
+
- All results are historical backtests, not guaranteed future performance
|
| 177 |
+
|
| 178 |
+
### Maintenance
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| 179 |
+
- Retrain monthly with fresh data
|
| 180 |
+
- Monitor capital preservation metrics
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| 181 |
+
- Adjust confidence thresholds based on market conditions
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| 182 |
+
- Validate recovery mechanisms effectiveness
|
| 183 |
+
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| 184 |
+
---
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| 185 |
+
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| 186 |
+
*Romeo V7 represents a significant advancement in algorithmic trading with a focus on capital preservation and consistent profitability.*
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