Experimental MT5 EA using Gemini Flash API - GeminiCommanderEA_v1

1. AxiomEdge_PRO_V211_2_STREAMLINED.py.txt

By simplifying the feature engineering, I removed a layer of pre-defined complexity and shifted the "intelligence" to the AI's dynamic pattern discovery.

Complex Features Left Out

The feature engineering process was streamlined by removing functions that calculate highly specialized, derivative, or complex indicators. These often represent fixed, pre-conceived ideas about market behaviour. The core idea is to provide the AI with fundamental building blocks rather than complex, opinionated signals.

The main categories of removed features include:

1. Advanced Statistical & Econometric Models: Features derived from models like GARCH (for volatility forecasting), PACF (autocorrelation), Hawkes Processes (self-excitation), and information theory metrics like Shannon Entropy were excluded.
2. Frequency & Cyclical Analysis: Complex signal processing techniques like Fourier, Wavelet, and Hilbert Transforms, which attempt to find dominant market cycles, were removed.
3. Market Microstructure Features: Proxies for order flow, market depth, and liquidity (e.g., volume_imbalance, estimated_spread) were left out. These are often noisy and difficult to model without true Level 2 data.
4. Pre-defined Technical Patterns: Specific, hardcoded patterns like trend pullbacks and indicator divergences were removed, as the AI is now tasked with discovering these types of relationships on its own.
5. Advanced Volatility Metrics: Academic volatility estimators like Parkinson and Yang-Zhang were replaced by simpler, more robust metrics like ATR and realized volatility.
6. Signal Enhancement: Post-processing steps like applying a Kalman Filter to smooth indicators were removed to provide the AI with more raw, unfiltered inputs.

Benefits of AI-Discovered Features

Introducing a larger set of AI-discovered features built from a "bare essential" base has several key advantages:

1. Adaptability to Market Regimes: This is the most significant benefit. The AI generates features specifically tailored to the current diagnosed market regime (e.g., "Trending," "Ranging"). Hardcoded features are static and may fail when market conditions change, whereas the AI's features are dynamic and context-aware.
2. Discovery of Novel Patterns: The AI can identify complex, non-linear relationships between fundamental features (e.g., how session times interact with volatility and statistical skew) that a human might never think to code. It moves beyond standard technical analysis to find a unique, data-driven "edge."
3. Reduced Human Bias: The extensive list of removed features represents a developer's specific hypotheses about what drives markets. By reducing this list and empowering the AI, we lessen the influence of preconceived biases and allow the model to learn directly from the data's underlying structure.
4. Hypothesis Generation: The AI is prompted to provide a clear, testable hypothesis for every feature it creates. This transforms it from a "black box" into a powerful research assistant, helping you understand why a particular signal might be predictive and providing new avenues for strategy development.

The Updated Function 'def discover_behavioral_patterns'

Modify the 'Discovery number' in the OBJECTIVE to increase or decease the AI Discovered Features. Currently set between 15-25

def discover_behavioral_patterns(self, base_features: List[str], diagnosed_regime: str) -> Dict[str, Dict[str, str]]:
        """Engages the AI to discover novel non-technical or behavioral patterns."""
        logger.info("-> Engaging AI to discover novel behavioral/statistical patterns...")
        if not self.api_key_valid:
            logger.warning("  - API key invalid or missing.")
            return {}
        # --- Step 1: Tag features into conceptual categories ---
        feature_categories = {
            "Time-Based": ['hour', 'day_', 'session', 'month', 'week_'],
            "Statistical & Fractal": ['skew', 'kurtosis', 'entropy', 'hurst', 'pacf', 'garch', 'zscore'],
            "Volatility": ['volatility', 'ATR', 'bollinger_bandwidth', 'bb_width', 'vix'],
            "Structural & Event-Based": ['gap', 'displacement', 'breakout', 'structural_break', 'orb_'],
            "Sentiment & Emotion": ['sentiment', 'fear_greed'],
            "Behavioral Insights": ['drawdown_percent', 'anchor_price', 'insidebar'],
            "Fundamental Price/Volume": ['Close', 'Open', 'High', 'Low', 'RealVolume', 'pct_change', 'prev_session_high', 'orb_high']
        }
        categorized_features = {
            cat_name: [f for f in base_features if any(key in f for key in keywords)]
            for cat_name, keywords in feature_categories.items()
        }
        # Logging categorized features
        for category, feats in categorized_features.items():
            logger.info(f"  - {category}: {len(feats)} features")
        # --- Step 2: Build the prompt to the AI ---
        category_strings = "\n".join(
            [f"{i+1}. {cat}: {json.dumps(feats)}"
             for i, (cat, feats) in enumerate(categorized_features.items())]
        )
        prompt = f"""
    You are a Quantitative Behavioral Analyst specializing in financial markets.
    ---
    **OBJECTIVE**:
    Discover 15-25 novel, non-obvious meta-features by combining base market features to capture **behavioral**, **time-sensitive**, or **statistical** phenomena. Avoid standard TA signals.
    ---
    **CURRENT MARKET REGIME**:
    - `{diagnosed_regime}` — Tailor hypotheses accordingly.
    ---
    **FEATURE CONSTRUCTION RULES**:
    - Combine features from **at least two different categories**.
    - Include **a clear, testable hypothesis** for each feature.
    - Use **safe lambda code**:
      - Start with `lambda row:`
      - Use `row.get('feature', default_value)`
      - Use `max(..., 1e-6)` to avoid zero division.
    - Do NOT use typical TA crossovers (e.g., MACD, RSI).
    - Use creative combinations that reflect **market psychology, volatility structure, sentiment reaction**, etc.
    ---
    **AVAILABLE FEATURE CATEGORIES**:{category_strings}
    ---
    **RESPONSE FORMAT** (Return exactly this JSON format with 15–25 entries):
    ```json
    {{
      "feature_name_example": {{
        "lambda": "lambda row: ...",
        "description": "HYPOTHESIS: ..."
      }}
    }}
    """
        # --- Step 3: Send prompt to model and parse response ---
        response_text = self._call_gemini(prompt)
        suggestions = self._extract_json_from_response(response_text)
        
        # --- Step 4: Validate and return ---
        if isinstance(suggestions, dict) and all(isinstance(v, dict) and 'lambda' in v and 'description' in v for v in suggestions.values()):
            logger.info(f"  - AI successfully proposed {len(suggestions)} new behavioral patterns.")
            return suggestions
        logger.error("  - AI failed to return a valid dictionary of behavioral patterns.")
        return {}

What Makes AxiomEdge Unique

AI-Powered Intelligence

1. AI Doctor: Gemini AI continuously monitors performance and suggests optimizations
2. Intelligent Parameter Tuning: AI-guided hyperparameter optimization with drift detection
3. Regime-Aware Adaptation: Automatic strategy adjustment based on market conditions
4. Natural Language Strategy Analysis: AI provides human-readable insights and recommendations

Advanced Telemetry & Monitoring

1. Real-Time Telemetry: Comprehensive JSONL-based telemetry system tracking every aspect of performance
2. Session Management: Complete session tracking with historical performance comparison
3. Health Monitoring: System component health tracking with automated alerts
4. Performance Attribution: Detailed cycle-by-cycle performance breakdown and analysis

Scientific Rigor

1. Walk-Forward Analysis: Robust out-of-sample validation with multiple cycles
2. SHAP 'Explainability': Feature importance analysis with SHAP values for model transparency
3. Multi-Task Learning: Simultaneous prediction of multiple market outcomes
4. Statistical Validation: Comprehensive statistical testing and validation protocols

Evolutionary Strategy Development

1. Genetic Programming: Automatic discovery of trading rules through evolutionary algorithms
2. Dynamic Ensembles: Adaptive model weighting based on performance and feature importance
3. Strategy Evolution: Continuous improvement of trading strategies through AI-guided evolution
4. Rule Complexity Management: Intelligent balance between strategy complexity and performance

Production-Ready Architecture

1. Modular Design: Each component can be used independently or as part of the complete framework
2. Scalable Processing: Multi-core parallel processing for large datasets
3. Memory Efficiency: Optimized data structures and streaming telemetry to prevent memory issues
4. Error Recovery: Robust error handling with graceful degradation and automatic recovery

Comprehensive Feature Engineering

1. 200+ Features: Extensive technical, statistical, and behavioural feature library
2. Multi-Timeframe Analysis: Automatic integration of features across multiple timeframes
3. Pattern Recognition: Advanced candlestick and price pattern detection
4. Entropy & Cycle Analysis: Sophisticated market microstructure analysis

Intelligent Automation

1. Framework Memory: Historical performance tracking with continuous learning
2. Adaptive Thresholds: Dynamic confidence thresholds based on market conditions
3. Intervention Protocols: Automated intervention when performance degrades
4. Parameter Drift Detection: Monitoring and alerting for strategy parameter drift

Professional Reporting

1. Multi-Format Reports: Text, HTML, and interactive dashboard generation
2. Publication-Quality Visualizations: High-resolution charts and plots
3. Executive Summaries: Concise performance summaries for stakeholders
4. Regulatory Compliance: Detailed audit trails and performance attribution

License & Attribution

This project is released under the GNU General Public License v3.0 (GPL-3.0). While you are free to use, modify, and distribute this software under the GPL-3.0 terms, it would be nice to be acknowledged for the original code in any further developments or public-facing projects that build upon it. A simple credit or link back to the original repository is greatly appreciated.

Architecture Overview
axiom_edge/
├── __init__.py # Main package exports
├── config.py # Configuration and validation
├── data_handler.py # Data collection and caching
├── ai_analyzer.py # AI analysis with Gemini
├── feature_engineer.py # 200+ Feature engineering
├── model_trainer.py # ML model training
├── backtester.py # Advanced backtesting engine
├── genetic_programmer.py # Genetic algorithm optimization
├── report_generator.py # Report generation
├── framework_orchestrator.py # Complete framework orchestration
├── telemetry.py # Advanced telemetry & monitoring
├── tasks.py # Task-specific interfaces
└── utils.py # Utility functions

Implementation Status

1. ALL COMPONENTS FULLY IMPLEMENTED: Complete production-ready framework
2. Advanced Features: Telemetry, AI Doctor, Dynamic Ensembles, Walk-Forward Analysis
3. Comprehensive Analytics: 200+ features, SHAP analysis, Performance attribution

Available Tasks
1. Data Collection Task
Collect historical data from various sources (Alpha Vantage, Yahoo Finance, Polygon).

# Command line usage
python main.py --task data_collection --symbols AAPL,GOOGL --start 2023-01-01 --end 2024-01-01
# Python usage
from axiom_edge import DataCollectionTask
task = DataCollectionTask()
data = task.collect_data(["AAPL", "GOOGL"], "2023-01-01", "2024-01-01")
task.save_data(data, "collected_data/")

1. Multi-source data collection
2. Intelligent caching (in-memory + disk)
3. Multiple timeframes support
4. Data validation and cleaning

2. Broker Information Task
Collect broker spreads and trading costs with AI-powered analysis.

# Command line usage
python main.py --task broker_info --symbols EURUSD,GBPUSD --broker oanda
# Python usage
from axiom_edge import BrokerInfoTask
task = BrokerInfoTask()
spreads = task.collect_spreads(["EURUSD", "GBPUSD"], "oanda")
analysis = task.analyze_broker_costs(["EURUSD"], {"spreads": spreads})

1. Real-time spread collection
2. Multi-broker comparison
3. AI-powered cost analysis
4. Trading cost optimization recommendations

3. Backtesting Task
Backtest your own trading strategies with comprehensive performance analysis.

# Command line usage
python main.py --task backtest --data-file my_data.csv --strategy-config strategy.json
# Python usage
from axiom_edge import BacktestTask
task = BacktestTask()
strategy_rules = {"sma_short": 10, "sma_long": 30}
results = task.backtest_strategy(data, strategy_rules)

1. Custom strategy rule engine
2. Comprehensive performance metrics
3. Risk management integration
4. Parameter optimization

4. Feature Engineering Task
Engineer 200+ features from raw price data for machine learning models.

# Command line usage
python main.py --task features --data-file data.csv --output features.csv
# Python usage
from axiom_edge import FeatureEngineeringTask
task = FeatureEngineeringTask()
features = task.engineer_features(raw_data)
selected_features = task.select_features(features, "target_column")
# Advanced usage with custom configuration
from axiom_edge import FeatureEngineer, create_default_config
config = create_default_config("./")
config.EMA_PERIODS = [8, 13, 21, 34, 55, 89, 144]  # Fibonacci EMAs
config.RSI_STANDARD_PERIODS = [14, 21, 28]
feature_engineer = FeatureEngineer(config, {'base': 'D1'}, {})
features = feature_engineer.engineer_features(data, {'D1': data})
# Multi-task labeling
labeled_data = feature_engineer.label_data_multi_task(features)

Feature Categories (200+ Total):

1. Technical Indicators (40+): RSI, MACD, Bollinger Bands, Stochastic, ATR, EMAs
2. Price & Returns (30+): Momentum, ROC, price derivatives, gap analysis
3. Volume Analysis (20+): Volume ratios, correlations, accumulation/distribution
4. Statistical Moments (25+): Rolling statistics, skewness, kurtosis, quantiles
5. Time-based (15+): Session indicators, cyclical encoding, hour/day patterns
6. Pattern Recognition (20+): Candle patterns, OHLC ratios, doji detection
7. Advanced Analytics (15+): Entropy, cycle analysis, Hilbert transforms
8. Multi-timeframe (Variable): Higher timeframe trend, momentum, volatility

Multi-task Labelling System:

1. Signal Pressure Classification: 5-class system (Strong Buy to Strong Sell)
2. Timing Score: TP/SL hit prediction (-1 to 1 scale)
3. Pattern Labels: Bullish/Bearish engulfing pattern detection
4. Volatility Spike Detection: Future volatility prediction

Advanced Capabilities:

1. Parallel Processing: Multi-core feature engineering for large datasets
2. Multi-timeframe Fusion: Integrates features from multiple timeframes
3. Robust Data Handling: Missing value imputation, outlier detection
4. Configurable Parameters: Customizable periods, thresholds, and methods

5. Model Training Task
Train machine learning models with automated hyperparameter optimization and advanced validation.

# Command line usage
python main.py --task model_training --data-file features.csv
# Python usage
from axiom_edge import ModelTrainingTask
task = ModelTrainingTask()
model_results = task.train_model(features, target)
# Advanced usage with custom configuration
from axiom_edge import ModelTrainer, GeminiAnalyzer, create_default_config
config = create_default_config("./")
config.OPTUNA_TRIALS = 100
config.FEATURE_SELECTION_METHOD = "mutual_info"
config.SHADOW_SET_VALIDATION = True
config.CALCULATE_SHAP_VALUES = True
gemini_analyzer = GeminiAnalyzer()
trainer = ModelTrainer(config, gemini_analyzer)
# Train with labeled data (from feature engineering)
results = trainer.train_and_validate_model(labeled_data)
# Single model training with custom parameters
pipeline, f1_score, features, error = trainer.train_single_model(
    df_train=data,
    feature_list=feature_list,
    target_col='target_signal_pressure_class',
    model_type='classification',
    task_name='custom_model'
)

Core Capabilities:

1. XGBoost Integration: Optimized gradient boosting models
2. Hyperparameter Optimization: Optuna-based automated tuning
3. Feature Selection: Multiple methods (mutual_info, f_classif, custom)
4. Advanced Validation: Shadow set validation, cross-validation
5. SHAP Analysis: Feature importance and explainability
6. Class Balancing: Automatic class weight calculation
7. Multi-task Learning: Support for multiple target variables

Advanced Features:

1. Optuna Integration: Multi-objective hyperparameter optimization
2. Shadow Set Validation: Prevents overfitting with holdout validation
3. Feature Importance Analysis: Both model-based and SHAP-based
4. Threshold Optimization: Automatic optimal threshold finding
5. Robust Error Handling: Comprehensive failure detection and reporting
6. AI-Guided Optimization: Gemini AI integration for intelligent parameter tuning

Performance Metrics:

1. Classification: F1-score, Accuracy, Precision, Recall, AUC
2. Regression: RMSE, MAE, R², Explained Variance
3. Feature Analysis: SHAP values, permutation importance
4. Validation: Cross-validation scores, holdout performance

6. Genetic Programming Task
Evolve trading rules using genetic algorithms with AI-guided optimisation.

# Command line usage
python main.py --task genetic_programming --data-file features.csv
# Python usage
from axiom_edge import GeneticProgrammer, FeatureEngineeringTask
# Create gene pool from features
feature_task = FeatureEngineeringTask()
features = feature_task.engineer_features(raw_data)
gene_pool = {
    'continuous_features': ['RSI_14', 'EMA_20', 'EMA_50', 'MACD', 'ATR'],
    'state_features': ['is_ny_session', 'day_of_week'],
    'comparison_operators': ['>', '<', '>=', '<='],
    'state_operators': ['==', '!='],
    'logical_operators': ['AND', 'OR'],
    'constants': [20, 30, 50, 70, 80]
}
# Initialize genetic programmer
gp = GeneticProgrammer(
    gene_pool=gene_pool,
    config=config,
    population_size=50,
    generations=25,
    mutation_rate=0.1,
    crossover_rate=0.7
)
# Evolve trading rules
best_chromosome, best_fitness = gp.run_evolution(features)
print(f"Best Long Rule: {best_chromosome[0]}")
print(f"Best Short Rule: {best_chromosome[1]}")
print(f"Fitness (Sharpe): {best_fitness:.4f}")

Core Capabilities:

1. Genetic Algorithm: Tournament selection, crossover, mutation
2. Fitness Evaluation: Sharpe ratio-based performance measurement
3. AI Integration: Gemini AI-guided gene pool optimization
4. Parallel Processing: Multi-core fitness evaluation
5. Rule Evolution: Automatic trading rule discovery
6. Configurable Parameters: Population size, generations, mutation rates

Evolution Operations:

1. Selection: Tournament selection for parent choosing
2. Crossover: Logical operator-based rule recombination
3. Mutation: Feature, operator, and constant mutations
4. Fitness: Risk-adjusted return evaluation with trade frequency penalties
5. Elitism: Best individuals preserved across generations

Advanced Features:

1. Gene Pool Optimization: AI-driven feature and operator selection
2. Rule Complexity Control: Depth-limited rule generation
3. Semantic Correctness: Type-aware feature and operator matching
4. Performance Analytics: Detailed evolution statistics and tracking
5. Retry Mechanisms: AI-guided gene pool fixes for failed evolutions

7. Report Generation Task
Generate comprehensive performance reports with visualizations and detailed analytics.

# Command line usage
python main.py --task report_generation --trades-file trades.csv --equity-file equity.csv
# Python usage
from axiom_edge import ReportGenerator, create_default_config
config = create_default_config("./")
config.nickname = "My Strategy"
config.REPORT_LABEL = "V2.1_PRODUCTION"
report_gen = ReportGenerator(config)
# Generate comprehensive report
metrics = report_gen.generate_full_report(
    trades_df=trades_df,
    equity_curve=equity_curve,
    cycle_metrics=cycle_metrics,
    aggregated_shap=shap_data,
    last_classification_report=classification_report
)
# Generate individual components
report_gen.plot_equity_curve(equity_curve)
report_gen.plot_shap_summary(shap_data)
report_gen.plot_trade_analysis(trades_df)
report_gen.generate_html_report(metrics, trades_df, equity_curve, shap_data)
# Quick summary
summary = report_gen.generate_summary_stats(metrics)
print(summary)

Core Capabilities:

1. Text Reports: Professional formatted performance reports
2. Static Visualizations: Matplotlib-based charts and plots
3. Interactive Dashboards: Plotly-based HTML dashboards
4. Performance Metrics: Comprehensive risk-adjusted analytics
5. Feature Analysis: SHAP importance visualization
6. Trade Analysis: Detailed trade breakdown and statistics

Visualization Types:

1. Equity Curve: Portfolio value over time with drawdown analysis
2. SHAP Summary: Feature importance horizontal bar charts
3. Trade Analysis: PnL distribution, cumulative returns, monthly breakdown
4. Interactive Dashboard: Multi-panel Plotly dashboard with zoom/pan
5. Risk Metrics: Drawdown periods, volatility analysis
6. Performance Attribution: Cycle-by-cycle breakdown

Report Sections:

1. Executive Summary: Key performance metrics and returns
2. Performance Metrics: CAGR, Sharpe, Sortino, Calmar ratios
3. Trade Analysis: Win rate, profit factor, average win/loss
4. Risk Analysis: Maximum drawdown, volatility measures
5. Walk-Forward Cycles: Cycle-by-cycle performance breakdown
6. Feature Importance: Top SHAP features with importance scores
7. Model Performance: Classification reports and validation metrics

Output Formats:

1. Text Reports: Professional ASCII-formatted reports (90-char width)
2. PNG Images: High-resolution charts (300 DPI)
3. HTML Dashboards: Interactive Plotly visualizations
4. Summary Stats: Concise performance summaries
5. CSV Exports: Detailed metrics for further analysis

9. Telemetry & Monitoring
Advanced telemetry system for comprehensive framework monitoring and analysis.

# Python usage
from axiom_edge import TelemetryCollector, TelemetryAnalyzer
# Initialize telemetry collection
telemetry = TelemetryCollector("logs/telemetry.jsonl")
# Log cycle completion
telemetry.log_cycle_data(
    cycle_num=1,
    status="completed",
    config_snapshot=config,
    labeling_summary=labeling_data,
    training_summary=training_results,
    backtest_metrics=performance_metrics,
    horizon_metrics=forward_metrics,
    ai_notes="Strong momentum signals detected"
)
# Log AI interventions
telemetry.log_ai_intervention(
    intervention_type="strategic",
    trigger_reason="performance_degradation",
    ai_analysis={"recommendation": "reduce_position_size"},
    action_taken={"position_size_multiplier": 0.8}
)
# Log performance milestones
telemetry.log_performance_milestone(
    milestone_type="new_equity_high",
    metrics=current_metrics,
    comparison_baseline=previous_best
)
# Analyze telemetry data
analyzer = TelemetryAnalyzer("logs/telemetry.jsonl")
trends = analyzer.analyze_cycle_performance_trends()
ai_effectiveness = analyzer.get_ai_intervention_effectiveness()
# Export session data
telemetry.export_session_data("csv")
telemetry.close_session()

Telemetry Capabilities:

1. Cycle Tracking: Complete cycle-by-cycle performance monitoring
2. AI Intervention Logging: Track AI decisions and their effectiveness
3. Performance Milestones: Automatic detection and logging of achievements
4. System Health: Component health monitoring with alerts
5. Evolution Tracking: Genetic programming progress monitoring
6. Regime Detection: Market regime changes and strategy adaptations

Advanced Analytics:

1. Performance Trends: Statistical analysis of performance evolution
2. AI Effectiveness: Measure impact of AI interventions
3. Feature Evolution: Track feature importance changes over time
4. Session Management: Complete session lifecycle tracking
5. Export Capabilities: Multiple format exports (JSON, CSV, Parquet)

10. Complete Framework Task
Orchestrate the complete AxiomEdge framework with walk-forward analysis and integrated workflow.

# Command line usage
python main.py --task complete --data-files "data/*.csv" --config config.json
# Python usage
from axiom_edge import FrameworkOrchestrator, CompleteFrameworkTask, create_default_config
# Using the orchestrator directly
config = create_default_config("./")
config.nickname = "Production Strategy"
config.TRAINING_WINDOW_DAYS = 252  # 1 year training
config.TEST_WINDOW_DAYS = 63       # 3 months testing
config.WALK_FORWARD_STEP_DAYS = 21 # 1 month step
config.MAX_WALK_FORWARD_CYCLES = 10
config.ENABLE_GENETIC_PROGRAMMING = True
orchestrator = FrameworkOrchestrator(config)
# Run complete framework
results = orchestrator.run_complete_framework(
    data_files=["data/AAPL.csv", "data/GOOGL.csv"],
    symbols=None,  # Or specify symbols for live data
    start_date="2023-01-01",
    end_date="2024-01-01"
)
# Using the task interface
task = CompleteFrameworkTask(config)
results = task.run_complete_framework(["data/AAPL.csv"])
# Single cycle analysis for testing
single_results = orchestrator.run_single_cycle_analysis(data)

Core Orchestration:

1. Data Pipeline: Automated data collection and preparation
2. Feature Engineering: 200+ features with multi-timeframe analysis
3. Model Training: Hyperparameter optimization and validation
4. Strategy Evolution: Genetic programming for rule discovery
5. Comprehensive Reporting: Performance analysis and visualization
6. Walk-Forward Analysis: Robust out-of-sample validation

Walk-Forward Analysis:

1. Training Windows: Configurable training period (default: 252 days)
2. Test Windows: Out-of-sample validation period (default: 63 days)
3. Step Size: Rolling window advancement (default: 21 days)
4. Cycle Management: Automated cycle execution and tracking
5. Performance Aggregation: Cross-cycle performance analysis
6. Early Intervention: Automatic failure detection and recovery

Intelligent Workflow:

1. Framework Memory: Historical performance tracking and learning
2. AI Integration: Gemini AI-guided optimization and adaptation
3. Error Recovery: Robust error handling with graceful degradation
4. State Management: Comprehensive framework state tracking
5. Performance Monitoring: Real-time cycle performance analysis

Comprehensive Analytics:

1. Cycle Breakdown: Per-cycle performance metrics and analysis
2. Feature Importance: Aggregated SHAP analysis across cycles
3. Model Performance: Classification reports and validation metrics
4. Strategy Evolution: Genetic programming results and rule discovery
5. Risk Analysis: Drawdown analysis and risk-adjusted metrics

Output Generation:

1. Text Reports: Professional performance reports with cycle breakdown
2. Visualizations: Equity curves, feature importance, trade analysis
3. Interactive Dashboards: HTML dashboards with drill-down capabilities
4. Data Exports: CSV exports for further analysis
5. Framework State: Serialized framework memory and configuration

# Command line usage
python main.py --task complete --data-files "data/*.csv" --config config.json
# Python usage
from axiom_edge import CompleteFrameworkTask
task = CompleteFrameworkTask()
results = task.run_complete_framework(data_files)

1. End-to-end automation
2. Walk-forward analysis
3. AI-driven optimization
4. Comprehensive reporting

Configuration
Configuration File Example

{
    "BASE_PATH": "./data",
    "REPORT_LABEL": "My_Strategy_v1",
    "INITIAL_CAPITAL": 10000.0,
    "operating_state": "conservative_baseline",
    "FEATURE_SELECTION_METHOD": "mutual_info",
    "TRAINING_WINDOW": "30D",
    "RETRAINING_FREQUENCY": "7D",
    "FORWARD_TEST_GAP": "1D",
    "LOOKAHEAD_CANDLES": 5,
    "RISK_CAP_PER_TRADE_USD": 500.0,
    "BASE_RISK_PER_TRADE_PCT": 0.02,
    "CONFIDENCE_TIERS": {
        "high": 1.5,
        "medium": 1.0,
        "low": 0.5
    }
}

[size=2]# Required for AI analysis
export GEMINI_API_KEY="your-gemini-api-key"[/size]
[size=2]# Required for data collection
export FINANCIAL_API_KEY="your-alpha-vantage-key"
# or
export POLYGON_API_KEY="your-polygon-key"[/size]
[size=2]# Optional: Broker API keys
export OANDA_API_KEY="your-oanda-key"
export IB_API_KEY="your-interactive-brokers-key"[/size]
[size=2][/size]

Use Cases

[size=2]from axiom_edge import FeatureEngineeringTask, DataCollectionTask[/size]
[size=2]# Collect data
data_task = DataCollectionTask()
raw_data = data_task.collect_data(["AAPL"], "2023-01-01", "2024-01-01")[/size]
[size=2]# Engineer features
feature_task = FeatureEngineeringTask()
features = feature_task.engineer_features(raw_data["AAPL"])[/size]
[size=2][/size]

[size=2]from axiom_edge import BacktestTask[/size]
[size=2]# Define your strategy
strategy = {
    "entry_condition": "RSI < 30 and SMA_10 > SMA_20",
    "exit_condition": "RSI > 70 or stop_loss_hit",
    "stop_loss_pct": 0.05,
    "take_profit_pct": 0.10
}[/size]
[size=2]# Backtest
task = BacktestTask()
results = task.backtest_strategy(data, strategy)[/size]
[size=2][/size]

[size=2]from axiom_edge import BrokerInfoTask[/size]
[size=2]# Compare brokers
task = BrokerInfoTask()
comparison = task.compare_brokers(
    symbols=["EURUSD", "GBPUSD", "USDJPY"],
    brokers=["oanda", "interactive_brokers", "forex_com"]
)[/size]
[size=2][/size]

[size=2]from axiom_edge import CompleteFrameworkTask[/size]
[size=2]# Run full framework
task = CompleteFrameworkTask(config)
results = task.run_complete_framework(["data/EURUSD_1D.csv"])[/size]
[size=2][/size]

Output Examples

[size=2]# Basic feature engineering
features = task.engineer_features(raw_data)
print(f"Generated {len(features.columns)} features")[/size]
[size=2]# Output: Generated 247 features
# Feature categories:
# - Technical Indicators: 42 features
# - Price & Returns: 31 features
# - Volume Analysis: 18 features
# - Statistical Moments: 28 features
# - Time-based: 16 features
# - Pattern Recognition: 22 features
# - Advanced Analytics: 15 features
# - Multi-timeframe: 35 features
# - Other: 40 features[/size]
[size=2][/size]

[size=2]labeled_data = feature_engineer.label_data_multi_task(features)[/size]
[size=2]# Signal Pressure Classes (5-class system)
print(labeled_data['target_signal_pressure_class'].value_counts())
# Output:
# 2 (Hold): 156 samples (42.9%)
# 1 (Sell): 89 samples (24.5%)
# 3 (Buy): 78 samples (21.4%)
# 0 (Strong Sell): 23 samples (6.3%)
# 4 (Strong Buy): 18 samples (4.9%)[/size]
[size=2]# Timing Scores (continuous -1 to 1)
print(labeled_data['target_timing_score'].describe())
# Output:
# mean: 0.023, std: 0.445, min: -1.0, max: 1.0[/size]
[size=2]# Pattern Detection
print(f"Bullish Engulfing: {labeled_data['target_bullish_engulfing'].sum()} occurrences")
print(f"Bearish Engulfing: {labeled_data['target_bearish_engulfing'].sum()} occurrences")
print(f"Volatility Spikes: {labeled_data['target_volatility_spike'].sum()} occurrences")[/size]
[size=2][/size]

:bar_chart: Cache Info: {
    'in_memory_items': 2,
    'disk_cache_files': 5,
    'cache_directory': 'data_cache',
    'total_cache_size_mb': 15.7
}

{
    "total_return": 0.157,
    "sharpe_ratio": 1.23,
    "max_drawdown": -0.08,
    "win_rate": 0.62,
    "total_trades": 45,
    "profit_factor": 1.8
}

{
    "regime": "TRENDING_BULL",
    "confidence": 0.85,
    "analysis": "Strong upward momentum with low volatility",
    "recommended_strategy": "Trend following with momentum confirmation",
    "key_indicators": ["RSI_oversold", "volume_expansion", "trend_strength"],
    "parameter_adjustments": {
        "risk_per_trade": 0.025,
        "stop_loss_pct": 0.04
    }
}

[size=2]:bar_chart: Feature Quality Report:
   Total features: 247
   Features with missing values: 12 (4.9%)
   High variance features: 89
   Low variance features: 23
   Highly correlated pairs (|r| > 0.9): 15[/size]
[size=2]:link: Top Feature Correlations:
   1. EMA_20 <-> EMA_21: 0.998
   2. RSI_14 <-> RSI_21: 0.892
   3. BB_upper_20 <-> BB_middle_20: 0.945[/size]
[size=2][/size]

Advanced Features

Comprehensive Feature Engineering

# Generate 200+ features with advanced analytics
from axiom_edge import FeatureEngineer, create_default_config
config = create_default_config("./")
config.EMA_PERIODS = [8, 13, 21, 34, 55, 89, 144, 233]  # Fibonacci sequence
config.RSI_STANDARD_PERIODS = [14, 21, 28]
config.LOOKAHEAD_CANDLES = 5
# Multi-timeframe feature engineering
timeframe_roles = {'base': 'D1', 'higher': 'W1'}
feature_engineer = FeatureEngineer(config, timeframe_roles, {})
# Prepare multi-timeframe data
data_by_tf = {
    'D1': daily_data,    # Base timeframe
    'W1': weekly_data,   # Higher timeframe for trend context
    'H4': hourly_data    # Lower timeframe for entry timing
}
# Engineer comprehensive feature set
features = feature_engineer.engineer_features(daily_data, data_by_tf)
print(f"Generated {len(features.columns)} features across multiple timeframes")
# Generate multi-task labels
labeled_data = feature_engineer.label_data_multi_task(features)
print("Labels generated:")
print(f"- Signal Pressure Classes: {labeled_data['target_signal_pressure_class'].value_counts()}")
print(f"- Timing Scores: {labeled_data['target_timing_score'].describe()}")

Advanced Feature Analysis

[size=2]# Analyze feature quality and relationships
def analyze_features(features):
    # Feature categories
    categories = {
        "Technical Indicators": [col for col in features.columns if any(x in col.lower() for x in ['rsi', 'macd', 'ema', 'bb_', 'atr'])],
        "Volume Analysis": [col for col in features.columns if 'volume' in col.lower()],
        "Statistical Moments": [col for col in features.columns if any(x in col.lower() for x in ['mean', 'std', 'skew', 'kurt'])],
        "Pattern Recognition": [col for col in features.columns if any(x in col.lower() for x in ['doji', 'wick', 'body', 'candle'])],
        "Time-based": [col for col in features.columns if any(x in col.lower() for x in ['hour', 'day', 'session'])],
        "Advanced Analytics": [col for col in features.columns if any(x in col.lower() for x in ['entropy', 'cycle', 'fourier'])]
    }[/size]
[size=2]    for category, features_list in categories.items():
        print(f"{category}: {len(features_list)} features")[/size]
[size=2]    return categories[/size]
[size=2]feature_categories = analyze_features(features)[/size]
[size=2][/size]

Custom Strategy Development with Rich Features

[size=2]# Define advanced strategy using engineered features
def advanced_strategy_rules(features):
    signals = pd.DataFrame(index=features.index)[/size]
[size=2]    # Multi-indicator confluence
    rsi_oversold = features['RSI_14'] < 30
    bb_squeeze = features['BB_width_20'] < features['BB_width_20'].rolling(20).quantile(0.2)
    volume_spike = features['volume_ma_ratio'] > 1.5
    trend_up = features['EMA_20'] > features['EMA_50'][/size]
[size=2]    # Advanced pattern recognition
    bullish_divergence = (features['Close'] < features['Close'].shift(5)) & (features['RSI_14'] > features['RSI_14'].shift(5))
    volatility_expansion = features['ATR'] > features['ATR'].rolling(20).mean() * 1.2[/size]
[size=2]    # Generate signals with confluence
    signals['signal'] = 0
    buy_conditions = rsi_oversold & trend_up & volume_spike & bullish_divergence
    sell_conditions = ~trend_up & volatility_expansion[/size]
[size=2]    signals.loc[buy_conditions, 'signal'] = 1
    signals.loc[sell_conditions, 'signal'] = -1[/size]
[size=2]    # Add confidence scoring
    signals['confidence'] = 0.0
    signals.loc[buy_conditions, 'confidence'] = (
        features.loc[buy_conditions, 'RSI_14'].apply(lambda x: (30-x)/30) * 0.3 +
        features.loc[buy_conditions, 'volume_ma_ratio'].apply(lambda x: min(x/2, 1)) * 0.3 +
        0.4  # Base confidence
    )[/size]
[size=2]    return signals[/size]
[size=2]# Use with backtester
task = BacktestTask()
results = task.backtest_strategy(features, advanced_strategy_rules)[/size]
[size=2][/size]

Multi-Asset Portfolio with Feature Engineering

[size=2]# Collect and engineer features for multiple assets
symbols = ["AAPL", "GOOGL", "MSFT", "TSLA", "SPY"]
data_task = DataCollectionTask()
feature_task = FeatureEngineeringTask()[/size]
[size=2]portfolio_features = {}
for symbol in symbols:
    print(f"Processing {symbol}...")[/size]
[size=2]    # Collect data
    raw_data = data_task.collect_data([symbol], "2023-01-01", "2024-01-01")[/size]
[size=2]    # Engineer features
    features = feature_task.engineer_features(raw_data[symbol])
    portfolio_features[symbol] = features[/size]
[size=2]    print(f"  Generated {len(features.columns)} features for {symbol}")[/size]
[size=2]# Cross-asset feature analysis
def calculate_cross_asset_features(portfolio_features):
    # Calculate correlation features between assets
    symbols = list(portfolio_features.keys())[/size]
[size=2]    for i, symbol1 in enumerate(symbols):
        for symbol2 in symbols[i+1:]:
            # Price correlation
            corr_20 = portfolio_features[symbol1]['Close'].rolling(20).corr(
                portfolio_features[symbol2]['Close']
            )
            portfolio_features[symbol1][f'corr_{symbol2}_20'] = corr_20[/size]
[size=2]            # Volatility correlation
            vol_corr = portfolio_features[symbol1]['ATR'].rolling(20).corr(
                portfolio_features[symbol2]['ATR']
            )
            portfolio_features[symbol1][f'vol_corr_{symbol2}_20'] = vol_corr[/size]
[size=2]calculate_cross_asset_features(portfolio_features)[/size]
[size=2][/size]

Development & Customisation

# Extend the FeatureEngineer class
class CustomFeatureEngineer(FeatureEngineer):
    def _calculate_custom_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
        """Add your custom technical indicators"""
        # Custom momentum indicator
        df['custom_momentum'] = (df['Close'] / df['Close'].shift(10) - 1) * 100
        # Custom volatility measure
        df['custom_volatility'] = df['Close'].rolling(20).std() / df['Close'].rolling(20).mean()
        # Custom pattern detection
        df['custom_pattern'] = (
            (df['Close'] > df['Open']) &
            (df['Close'].shift(1) < df['Open'].shift(1)) &
            (df['Volume'] > df['Volume'].rolling(20).mean() * 1.5)
        ).astype(int)
        return df
    def _process_single_symbol_stack(self, symbol_data_by_tf, macro_data=None):
        # Call parent method first
        df = super()._process_single_symbol_stack(symbol_data_by_tf, macro_data)
        # Add custom features
        if df is not None:
            df = self._calculate_custom_indicators(df)
        return df
# Use custom feature engineer
config = create_default_config("./")
custom_engineer = CustomFeatureEngineer(config, {'base': 'D1'}, {})
features = custom_engineer.engineer_features(data, {'D1': data})

# Create custom task for specialized analysis
class TechnicalAnalysisTask(BaseTask):
    def __init__(self, config):
        super().__init__(config)
        self.feature_engineer = FeatureEngineer(config, {'base': 'D1'}, {})
    def analyze_support_resistance(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Custom support/resistance analysis"""
        # Engineer features first
        features = self.feature_engineer.engineer_features(data, {'D1': data})
        # Find support/resistance levels
        highs = features['High'].rolling(20).max()
        lows = features['Low'].rolling(20).min()
        current_price = features['Close'].iloc[-1]
        resistance = highs.iloc[-1]
        support = lows.iloc[-1]
        return {
            'current_price': current_price,
            'resistance': resistance,
            'support': support,
            'distance_to_resistance': (resistance - current_price) / current_price,
            'distance_to_support': (current_price - support) / current_price,
            'rsi_current': features['RSI_14'].iloc[-1],
            'trend_strength': features['EMA_20'].iloc[-1] - features['EMA_50'].iloc[-1]
        }
    def generate_trading_signals(self, data: pd.DataFrame) -> pd.DataFrame:
        """Generate custom trading signals"""
        features = self.feature_engineer.engineer_features(data, {'D1': data})
        signals = pd.DataFrame(index=features.index)
        signals['signal'] = 0
        signals['confidence'] = 0.0
        # Multi-factor signal generation
        rsi_oversold = features['RSI_14'] < 30
        rsi_overbought = features['RSI_14'] > 70
        trend_up = features['EMA_20'] > features['EMA_50']
        volume_spike = features['volume_ma_ratio'] > 1.3
        # Buy signals
        buy_conditions = rsi_oversold & trend_up & volume_spike
        signals.loc[buy_conditions, 'signal'] = 1
        signals.loc[buy_conditions, 'confidence'] = 0.8
        # Sell signals
        sell_conditions = rsi_overbought & ~trend_up
        signals.loc[sell_conditions, 'signal'] = -1
        signals.loc[sell_conditions, 'confidence'] = 0.7
        return signals
# Use custom task
task = TechnicalAnalysisTask(config)
analysis = task.analyze_support_resistance(data)
signals = task.generate_trading_signals(data)

# Add custom data source
class CustomDataHandler(DataHandler):
    def _fetch_custom_crypto_source(self, symbol, start_date, end_date):
        """Custom cryptocurrency data source"""
        # Implement your custom API integration
        import requests
        url = f"https://api.custom-crypto.com/v1/ohlcv"
        params = {
            'symbol': symbol,
            'start': start_date,
            'end': end_date,
            'interval': '1d'
        }
        response = requests.get(url, params=params)
        data = response.json()
        # Convert to DataFrame
        df = pd.DataFrame(data['ohlcv'])
        df.columns = ['timestamp', 'Open', 'High', 'Low', 'Close', 'Volume']
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df.set_index('timestamp', inplace=True)
        return df
    def get_crypto_data(self, symbol, start_date, end_date):
        """Get cryptocurrency data"""
        return self._fetch_custom_crypto_source(symbol, start_date, end_date)
# Use custom data handler
handler = CustomDataHandler()
btc_data = handler.get_crypto_data("BTC-USD", "2023-01-01", "2024-01-01")

Testing & Examples

# Complete framework demonstration
python examples/complete_framework_demo.py
# Feature engineering demonstration
python examples/feature_engineering_demo.py
# Model training demonstration
python examples/model_training_demo.py
# Genetic programming demonstration
python examples/genetic_programming_demo.py
# Report generation demonstration
python examples/report_generation_demo.py
# Command line interface
python main.py --task complete --data-files "data/*.csv"

# Test feature engineering performance
import time
from axiom_edge import FeatureEngineeringTask
# Create large dataset
large_data = create_realistic_market_data(5000)  # 5000 days
# Time feature engineering
start_time = time.time()
task = FeatureEngineeringTask()
features = task.engineer_features(large_data)
end_time = time.time()
print(f"Processed {len(large_data)} samples in {end_time - start_time:.2f} seconds")
print(f"Generated {len(features.columns)} features")
print(f"Processing rate: {len(large_data) / (end_time - start_time):.0f} samples/second")

Phase 1: Modular Foundation

1. Modular package structure
2. Configuration system
3. Data handler
4. AI analyzer
5. Task interfaces
6. Basic examples

Phase 2: Core Component Extraction

1. Extract FeatureEngineer from monolithic file
2. Extract ModelTrainer from monolithic file
3. Extract Backtester from monolithic file
4. Extract GeneticProgrammer from monolithic file
5. Extract ReportGenerator from monolithic file
6. Extract TelemetryCollector from monolithic file

Phase 3: Framework Integration

1. Create FrameworkOrchestrator
2. Implement walk-forward analysis
3. Add comprehensive examples
4. Professional documentation

Phase 4: Advanced Features

1. Web interface
2. Real-time trading integration
3. Advanced visualization
4. Cloud deployment support

Congratulations Lionesses!

UEFA Women's Euro Winners 2025

Proud day to be English

Explaining the complexity behind how AxiomEdge Develops and Discovers its own strategies

1. Combined data for H4: 33,668 rows for 4 symbols
2. Combined data for M15: 400,692 rows for 4 symbols
3. Combined data for H1: 134,656 rows for 4 symbols
4. Combined data for Daily: 5,612 rows for 4 symbols

The process begins by loading and combining the raw market data.

1. Files Loaded: 16
2. Financial Instruments: 4 (EURUSD, GBPUSD, AUDUSD, USDCAD)
3. Timeframes per Instrument: 4 (15-Minute, 1-Hour, 4-Hour, Daily)
4. Total Raw Rows: When all files are combined, the system processes 574,628 individual rows, or "moments in time".
5. Total Raw Values: Each raw row starts with 8 columns of data (Timestamp, OHLC, etc.), meaning the initial dataset contains 574,628 × 8 = 4,597,024 individual values.

Engineered Data Dimensions (Depending on your dataset and what the AI decides)

The system then calculates new analytical data points for each raw row, for example.

1. Engineered Features ("Clues"): 191 (this will be different for you)
2. This expands the dataset's width significantly, turning the 8 initial columns into nearly 200 calculated indicators for every moment in time.

Data Analysed in a Single Training Cycle

For each training period, the AI analyses a specific slice of the engineered data. Using the numbers from a typical cycle in your log, the size is as follows:

1. Training Set: 139,739 rows × 191 features = 26,689,149 values
2. Validation (Shadow) Set: 67,488 rows × 191 features = 12,890,208 values

This means that to train and validate the models for just one cycle, the AI analyses a total of 39,579,357 individual data points.

The data is both multi-layered and multi-dimensional.

It's far more complex than a single layer of data.

Multi-Layer (Different "Zoom Levels")

The data is multi-layered because it analyses the market across four different timeframes simultaneously:

1. Daily
2. 4-Hour
3. 1-Hour
4. 15-Minute

Think of this like looking at a map. The "Daily" layer is the world view showing the major, long-term trend. The "15-Minute" layer is the street view, showing every small turn and detail. The AI uses all these "zoom levels" at once to understand the big picture and the immediate situation.

To illustrate this complexity, broken down by the system's decision-making stages.

1. The Feature Selection Space: Choosing the Right Clues

This is the most significant source of complexity. The system first creates a large pool of potential "clues" (features) and then must select the optimal combination to build a model.

1. Total Pool of Clues Available: 191
2. Typical Number of Clues Selected per Model: ~50

The number of possible ways to choose 50 clues from a pool of 191 is calculated using a combination formula (¹⁹¹C₅₀).

1. Total Possible Combinations of Clues: 8.2 x 10⁴²
   1. 8.2 Tredecillion (8,200,000,000,000,000,000,000,000,000,000,000,000,000,000)

To put that number in perspective, a 20-character password using 94 characters has about 5.8 x 10³⁹ combinations.

1. 5.8 Duodecillion (5,800,000,000,000,000,000,000,000,000,000,000,000,000)

2. The Hyperparameter Space: Tuning the AI's "Brain"

Once the clues are selected, the AI must then tune its own internal settings (hyperparameters) for that specific set of clues. For a typical run, this involves searching through:

1. ~14 options for the number of decision trees.
2. ~5 options for the depth of each tree.
3. ~10 options for the learning rate.
4. ~200 other possible combinations for settings like data sampling and regularisation.

Multiplying these together reveals the size of the search space for fine-tuning just one model:

1. Possible Model Configurations: 14 × 5 × 10 × 200 = 140,000

The "Infinite" Space of Clue Creation

The number of potential clues the system could theoretically create is practically infinite.

Think of it this way: the previous calculation was like figuring out how many different 50-card decks you could make from a fixed set of 191 unique cards.
Now is the question, how many unique cards could possibly exist in the first place?

For example, one of the 191 clues is "Recent Volatility," which is based on the last 14 time periods. We could also create separate clues for:

1. Volatility over the last 15 periods.
2. Volatility over the last 16 periods.
3. The ratio of 14-period volatility to 50-period volatility.
4. The volatility multiplied by the trend strength... and so on.

Each variation of a calculation creates a brand new, unique clue. Since there is no limit to the mathematical formulas and parameter combinations, the number of potential clues is endless.

How the Framework Handles This

Because the number of potential clues is infinite, the framework takes a smart, two-stage approach:

1. Intelligent Creation: It doesn't try to create every possible clue. Instead, it uses financial expertise to engineer a powerful and diverse set of 191 of the most proven and useful clues. This creates a large but manageable pool to work from.
2. Intelligent Selection: It then focuses its main effort on solving the enormous (but finite) problem of selecting the best possible combination of those 191 clues, which, as we calculated, has a complexity of 8.2 x 10⁴².

Think of this as one row in the final spreadsheet that the AI analyses. It combines the different layers and dimensions into one comprehensive snapshot.

Example: One Moment in Time

Let's imagine we're looking at the 15-minute price candle for the EUR/USD currency pair that closed at 10:30 AM. The AI doesn't just see the price; it sees this entire profile:

1. Basic Raw Data (The 15-Minute Candle)

1. Open Price: 1.0750
2. High Price: 1.0765
3. Low Price: 1.0748
4. Close Price: 1.0760
5. Volume: 1200

2. Multi-Layered Context (Data from other "Zoom Levels")

1. 1-Hour Trend: 1 (meaning the trend on the hourly chart is currently up)
2. 4-Hour RSI: 62 (showing moderate buying pressure on the 4-hour chart)
3. Daily Volatility (ATR): 85 (meaning the average daily range is 85 points)

3. Engineered Features (A sample of the 191 "Clues")

1. Recent Volatility (ATR on the 15-min chart): 12
2. Trend Strength (ADX): 45 (a strong trend is in progress)
3. Momentum (RSI): 68 (strong recent buying momentum)
4. Market Regime: 2 (categorised as a "trending" market type)
5. Is London Trading Session Active?: 1 (meaning Yes)
6. Is there a Bullish Pattern?: 1 (meaning Yes, a pullback was detected)
7. Feature from Genetic Programming: -1 (the evolved rule gives a "sell" signal)
8. ...and 184 other calculated clues.

The Final Complexity

So for that single 15-minute period, the AI sees a complete profile of 197 different numbers.
When you multiply this depth of information by the total number of moments in time from the most detailed timeframe, you arrive at the final figure:
400,692 (moments in time) × 197 (data columns per moment) = 78,936,324 total data points.

In summary, this is not just a backtester or some simple programme that creates a 70/30 RSI bounce strategy with 200 EMA crossover.

UPDATES

SLIDELINE PROJECT

1. Lower Tier 1: threshold = -2.04 → 0.01 lots
2. Lower Tier 2: threshold = -2.39 → 0.02 lots
3. Upper Tier 1: threshold = -0.64 → 0.01 lots
4. Upper Tier 2: threshold = -0.29 → 0.02 lots
5. Basis mean revert = -1.34
6. Lead-lag filter: signal required (UP for lower entries, DOWN for upper entries), expires in 25 minutes (5 × 5m bars), lookback 36 bars, threshold 1.5σ, with cooldown.

Equity Curve

The equity curves for both strategies would show positive growth, but their character would differ significantly.

1. Non-Hybrid: A steeper but more volatile climb, with noticeable dips corresponding to its 0.78% drawdown.
2. Enhanced Hybrid: A smoother, more consistent ascent with very shallow dips, reflecting its lower drawdown and higher win rate.

Execution and Risk Profile

1. Market Exposure: The Non-Hybrid strategy has significantly more market exposure due to its higher trade frequency, which increases its vulnerability to black swan events. The Hybrid strategy spends more time flat, reducing overall risk.
2. Transaction Costs: The Hybrid strategy would incur less than half the transaction costs (spreads/commissions) due to its lower trade volume, further improving its net performance in a live environment.
3. Position Sizing: While both strategies use the same tiered lot sizing, the Hybrid model's higher expectancy (~$1.65 vs. ~$1.05) means that every dollar risked per trade is working more efficiently.

Final Verdict

While the Non-Hybrid strategy is effective, the Enhanced Hybrid Strategy is demonstrably superior in every measure of quality and risk. It achieves a near-perfect win rate and an exceptional Profit Factor by sacrificing a small amount of gross profit. For long-term, stable growth, the Enhanced Hybrid strategy is the recommended implementation.

Consider that I have not included spreads or commissions.

1. Three-Tier Gemini LLM Analysis: The script uses a sophisticated, layered approach to trade analysis and management, making three distinct API calls to Gemini for different tasks.
   1. 1. Junior Analyst (M15 Strategist): Its primary role is to analyse the M15 and H1 timeframes to establish a directional bias (UP, DOWN, FLAT) or to validate a pattern found by the Playbook Engine. It acts as the initial filter and setup finder.
   2. 2. Senior Analyst (M5 Execution): This is the final decision-maker. It synthesizes the Junior Analyst's proposal, a script-based technical checklist, macro news, and risk factors to issue the final command: BUY, SELL, or HOLD.
   3. 3. Advisor AI (Trade Manager): A specialized analyst that is only activated when a trade is in profit. It analyses the open position's health and momentum to provide recommendations like exit_all, exit_partial, or tighten_stop to protect gains while letting winners run.

2. Enhanced Strategy Playbook & Pattern Matching: The script actively scans for predefined trade setups stored in strategy_playbook.json. If a pattern is matched, it is sent to the Junior Analyst for validation before the Senior Analyst considers execution. The default playbook in v9.14 includes:
   1. trend_continuation_breakout_long/short_v1
   2. trend_pullback_long/short_v1
   3. mean_reversion_long/short_v1

3. Dual-Pronged Trade Identification: The system can initiate trades in two ways, ensuring it never misses an opportunity.
   1. Playbook-Driven: Explicitly enters trades based on the high-probability patterns defined in the playbook.
   2. Heuristic-Driven: If no playbook pattern matches, the Junior and Senior analysts work together to find trades based on general trend alignment and market context, ensuring the script is not limited to only its coded strategies.

4. Multi-Timeframe "Custom Stack" Analysis: The core trading logic is built on a hierarchical analysis of three key timeframes to ensure alignment before entering a trade.
   1. H1 (Directional Filter): Establishes the overall market trend and bias (Bullish/Bearish).
   2. M15 (Setup Timeframe): Looks for a valid trade setup (e.g., a pullback or consolidation) that aligns with the H1 trend.
   3. M5 (Execution/Confirmation Timeframe): Provides the final confirmation and trigger for trade entry.

5. Adaptive Learning & Anti-Pattern Detection: The script features a "Learning Layer" designed to improve performance over time.
   1. It logs every trade's context and outcome.
   2. It creates a unique "fingerprint" for each setup (e.g., DIR:BUY;SESS:NY_Open;H1_BIAS:BULLISH...).
   3. If a specific fingerprint consistently results in losses (e.g., >70% loss rate over 3+ trades), it is flagged as an "Anti-Pattern," and the Senior Analyst will be warned against taking similar trades in the future.

6. Dynamic and Hybrid Risk Management: Stop Loss (SL) and Take Profit (TP) levels are calculated dynamically for each trade rather than using fixed values.
   1. Hybrid Stop Loss: The initial SL is calculated using a hybrid model that anchors to the H1 structural price (e.g., the low of the last 12 hours for a buy) and uses the M15 Average True Range (ATR) as a volatility buffer. A minimum point value is enforced to prevent stops from being too tight.
   2. Dynamic Take Profit: The TP is set based on the calculated SL points and a configurable risk-to-reward ratio (e.g., 1.5R).
   3. In-Trade SL Management: The Advisor AI can recommend modifying the SL of an open trade to lock in profits (e.g., moving it to break-even or trailing it behind a recent swing).

7. Time Zone & Market Session Awareness: The script is aware of the market's 24-hour cycle and adjusts its behaviour accordingly.
   1. It operates on New York (America/New_York) time for all session logic.
   2. It automatically enters a CAUTIOUS_TRADING mode during high-volatility periods like the NY Open (09:30-10:30) and low-liquidity periods like the daily rollover (17:00-18:00).
   3. It enters a WEEKEND_SLEEP mode from Friday 17:00 NY time until the market re-opens, and will close all open positions before pausing.

8. Automated Daily Parameter Optimisation: The script is designed to work with an external optimizer.
   1. During the low-liquidity period between the NY close and the start of the next trading day, the main function calls a separate optimizer.py script.
   2. This optimizer analyses recent performance data to fine-tune key parameters (like ATR multipliers or risk/reward ratios).
   3. The main script then reloads these optimized parameters from optimised_parameters.json before resuming active trading.

9. Live Data Analysis & Execution Cycle: The script's core logic is triggered by market movement.
   1. The main loop continuously monitors the market for the formation of a new M5 candle.
   2. Upon the close of each M5 candle, the run_full_analysis_cycle is executed, ensuring the system is analysing fresh market data approximately every 5 minutes during active trading hours.

Why the timeframes?

It's just a test, running the script for a few weeks at time testing different timeframes and then different symbols will be required for full optimisation. It's currently only optimised for my broker and the SPY (SPX500). I will keep everyone posted.

1. Advanced JSON Parsing: MQL5 does not have built-in JSON parsing. We have implemented a basic string-based JSON extraction, but it is not robust for complex nested structures. In Python, we could use the json library to parse arbitrarily complex JSON. In MQL5, we are limited to simple key-value extraction.
2. Partial Position Closing: MQL5 does not support partial closing of positions natively. The CTrade class only allows closing the entire position. We have implemented a workaround by modifying the position size, but this is not the same as partial closing and may not be supported by all brokers.
3. Advanced Order Types: MQL5 does not support some of the advanced order types that might be available in other platforms (like bracket orders, OCO, etc.). We are using basic market orders with SL and TP.
4. Real-Time Data Streaming for Multiple Symbols: While MQL5 can handle multiple symbols, it requires explicit subscription and may not be as efficient as Python for streaming data for many symbols simultaneously.
5. External Data Sources: We are limited to the data provided by the broker. For example, if the broker does not provide VIX, we cannot get it. We have implemented a synthetic VIX, but it is not the same as the real VIX.
6. Machine Learning and Advanced Analytics: MQL5 does not have built-in machine learning libraries. We are relying on the Gemini AI for analysis.
7. Concurrency and Parallel Processing: MQL5 does not support multithreading. The entire EA runs in a single thread, so we cannot run the DetectorEngine in parallel with the main analysis.
8. Time Zone Handling: We have implemented manual time zone calculations, but it is error-prone and does not account for daylight saving time changes automatically.
9. File I/O Limitations: MQL5 file operations are limited to the sandboxed environment (unless using FILE_COMMON). Also, we cannot easily append to JSON files without reading the entire file first.
10. Error Handling and Recovery: MQL5 does not have the same level of exception handling as Python. We are using return codes and manual error checking.
11. WebRequest Limitations: The WebRequest function in MQL5 is synchronous and may block the execution. Also, it does not support all HTTP features (like custom headers fully) and may be restricted by the broker.
12. No Direct Market Depth Access: MQL5 does not provide easy access to market depth (level 2 data) for analysis.
13. Limited Indicator Access: We are using built-in indicators, but custom indicators require compilation and may not be as flexible as Python libraries.
14. No Direct Access to Economic Calendar: We have implemented a basic event proximity check (NFP, CPI), but it is hardcoded and not dynamic.
15. Optimizer Integration: We are not using the built-in optimizer because the EA logic is too complex for the standard MT5 optimizer. We have disabled the optimizer and rely on fixed parameters.

Workarounds and Solutions:

1. JSON Parsing: We can use third-party libraries or write a more robust parser, but it is complex. Alternatively, we can design the AI responses to be simple and predictable.
2. Partial Closing: We can use multiple positions to simulate partial closing, but it complicates the management.
3. Advanced Orders: We can simulate some order types by managing multiple orders, but it is not perfect.
4. Data Streaming: We can use the OnTick and OnBookEvent for the current symbol, but for multiple symbols, we need to use OnTick and check each symbol.
5. External Data: We can use WebRequest to fetch external data, but it is limited and may be slow.
6. Machine Learning: We are relying on the Gemini AI, so we don't need on-device ML.
7. Concurrency: We can use timers to run tasks in a pseudo-parallel way, but it is not true concurrency.
8. Time Zones: We can use the TimeGMTOffset function to get the broker's time zone offset, but it is not the same as the New York time. We are using hardcoded offsets.
9. File I/O: We are using FILE_COMMON to share files between EAs, but it is still limited.
10. Error Handling: We are using extensive logging and return code checking.
11. WebRequest: We are using the WebRequest function with a timeout and error handling.
12. Market Depth: We are not using market depth in the current strategy.
13. Indicators: We are using built-in indicators, which are sufficient.
14. Economic Calendar: We are using hardcoded events, but we can update them manually.

Conclusion
The missing 5% are mainly in the areas of advanced order management, partial position closing, robust JSON parsing, and real-time data for multiple symbols. However, the core functionality of the EA is implemented and should work as expected.
The EA remains highly effective for its intended purpose, but lacks the sophistication of a full Python implementation running on a dedicated server with unlimited resources.

How it Works

1. Detection: The main loop continuously checks for an active trade. The moment g_last_known_trade_id is no longer found, it knows the trade has closed.
2. Data Consolidation: It immediately calls the log_closed_trade_summary function. This is the core of the analysis, where it gathers all the critical details of the completed trade by looking up the historical orders and deals.
3. Logging & Learning: The consolidated trade data is then passed to the PerformanceMonitor, which saves the analysis to multiple files, each with a specific purpose.

The Log Outputs

The analysis of a closed trade is recorded in four distinct places:

1. Main Log File (SPX500.log): A concise, human-readable summary is printed directly to the main log for immediate feedback. It looks like this:
   INFO - Logged closed trade summary for #200930928: P/L=$XX.XX, R-Multiple=X.XX
2. Detailed Trade Record (trade_log.jsonl): A comprehensive JSON object containing every detail of the trade (entry/exit prices, times, profit, R-multiple, exit reason, etc.) is saved to this file. This creates a permanent, machine-readable history of every trade taken.
3. Learning Layer Memory (anti_pattern_memory.jsonl): The script's learning module analyzes the initial conditions of the trade, creates a unique "fingerprint" for that setup, and logs whether it resulted in a win or a loss. This file is crucial for the system to learn which market patterns are consistently failing.
4. Performance Dashboard (performance_summary.json): Finally, the overall system statistics, such as the rolling win rate and average R-multiple, are recalculated and updated in this file.

This is my work around to have live Machine Leaning functionality within GATE without having too many overheads and processes in the background. Including this trade fingerprinting there are patterns from playbook as well that has a logic for any trade setup which requires a confidence level based on the analysis that knows each trade is unique and we will never 100% match the same pattern so there is a +/-% the Gemini Flash considers first to say, 'This pattern I analysed is almost like the current market condition by X% and the rest of the analysis gives me Y% confidence so I'll enter the trade'. Then it is up to Gemini to manage the trade.

The logging system and logic works like the champion trade I have in Axiom and this trade fingerprinting compares previous trades with each new closed trade to see if it was better or worse than the champion that has similar patterns. Thus a new champion trade strategy can be found and replace the old champion which the new champion pattern is used and so on developing GATE to make smarter trading decisions with Gemini Flash.

1. The "Whoa, That's a Big Jump!" Rule: Has the price gapped open further than it would normally move in a few hours? If the jump is abnormally large and against your trade, it's a major red flag.
2. The "Instant Pain" Rule: Even if the jump isn't massive, has it already caused your trade to lose a significant chunk (e.g., 33%) of the money you were willing to risk?

If the answer to either of those questions is "yes," the Sentinel immediately closes the trade to prevent any more damage.
The Smart Part (The "Reversal"): The Sentinel doesn't just stop there. It realizes that a huge gap is powerful information. If it had to close your "SELL" trade because the market gapped UP violently, that's a very strong signal that the market wants to go UP. So, it sends a "CRITICAL ALERT" to the main AI brain, telling it, "We were just forced out by massive upward strength. You should immediately look for an opportunity to BUY."
In short, it’s a one-time check that turns a defensive risk-management action into an immediate trading opportunity.

2. The Failsafe: The "Are We Sure?" Backup Plan
Now, what happens if the Sentinel's first check says, "The gap is big, but maybe not dangerous enough to close the trade just yet. Let's watch it."? That old trade from Friday is now in a vulnerable position.
The Failsafe is the backup plan that runs continuously on every new 5-minute candle all through Monday. It keeps a close eye on that "surviving" weekend trade.
To take action, it needs a "YES" to all three of these questions:

1. Is this the old weekend trade we're worried about? (Yes)
2. Is the price still very far (more than 10 points) from where it closed on Friday? (Yes)
3. Is our main AI analyst now saying, with extremely high confidence, "We absolutely must trade in the OPPOSITE direction right now!"? (For example, the old trade is a BUY, but the AI sees a perfect V-shape pattern and wants to SELL immediately).

If it gets a "yes" to all three, the Failsafe triggers. It says, "Holding onto this old trade from Friday is a bad idea. All the new evidence is telling us to go the other way."
It then automatically closes the old, gapped trade to make way for the new, higher-confidence trade.
In simple terms, the Failsafe prevents the system from being stubborn. It stops it from clinging to an old plan from last week when the market on Monday is clearly screaming that it's time to do the exact opposite.

##LOG OUTPUT##
2025-10-06 10:55:12,779 - INFO - Successfully loaded optimized parameters: {'atr_sl_multiplier': 2.0, 'risk_reward_ratio': 1.2, 'quant_dev_score_threshold': 1.9}
2025-10-06 10:55:12,779 - INFO - Successfully loaded 6 plays from 'strategy_playbook.json'.
2025-10-06 10:55:12,779 - INFO - Successfully loaded 2 detector patterns.
2025-10-06 10:55:12,779 - INFO - Active trade #200930928 found. Executing Weekend Gap Sentinel immediately...
2025-10-06 10:55:12,779 - WARNING - Weekend Gap Sentinel: Detected trade #200930928 held over the weekend. Analyzing gap...
2025-10-06 10:55:12,779 - WARNING - Weekend Gap Sentinel: Override: Gap caused a loss of 0.34R, exceeding the 0.33R threshold.
2025-10-06 10:55:12,779 - CRITICAL - WEEKEND GAP SENTINEL : Override: Gap caused a loss of 0.34R, exceeding the 0.33R threshold.. Closing Position #200930928 IMMEDIATELY.
2025-10-06 10:55:12,779 - INFO - Closing all 1 positions for SPX500. Reason: Weekend Gap Sentinel
2025-10-06 10:55:13,030 - INFO - SUCCESS: Position 200930928 closed.
2025-10-06 10:55:13,030 - INFO - Weekend Gap Sentinel execution finished. Re-checking trade status...
2025-10-06 10:55:13,030 - WARNING - Trade found at startup was confirmed CLOSED by the Weekend Gap Sentinel.
2025-10-06 10:55:13,030 - INFO - --- Starting Gemini Trader v9.44 for SPX500 ---
2025-10-06 10:55:13,030 - WARNING - Sentinel-Reversal signal detected: BULLISH_REVERSAL. Triggering immediate analysis for counter-trade.
2025-10-06 10:55:13,030 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 10:55:13,030 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 10:55:14,811 - INFO - V-Shape Detector: Bearish Inverted V-Shape signal detected.
2025-10-06 10:55:14,811 - INFO - V-Shape Detector: Bearish Inverted V-Shape signal detected.
2025-10-06 10:55:14,811 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BEAR'.
2025-10-06 10:55:14,811 - INFO - Junior Analyst AI (M15 Strategist) analyzing...
2025-10-06 10:55:16,218 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.7)
2025-10-06 10:55:16,218 - INFO - Senior Analyst AI (M5 Execution) analyzing...
2025-10-06 10:55:18,030 - INFO - Senior Analyst AI M5 Execution Decision: SELL. Reason: CRITICAL PATTERN ALERT: BEARISH_V_REVERSAL takes precedence over all other signals. The M5 price action needs to be validated against the pivot price to confirm rejection strength.
2025-10-06 10:55:18,030 - INFO - Attempting to open SELL 0.02 lots of SPX500 @ 6731.18
2025-10-06 10:55:18,264 - INFO - SUCCESS: Order 200952638 sent. Finding position ID...
2025-10-06 10:55:19,265 - INFO - Position ID found: #200952638 (Linked to Order 200952638).
2025-10-06 10:55:19,265 - INFO - Initialization complete. Trader is running. Press Ctrl+C to exit.
2025-10-06 10:55:19,265 - INFO - Current Market Session: ACTIVE_TRADING (Risk: STANDARD) - Normal trading hours.

1. The Weekend Gap Sentinel correctly fired, creating a BULLISH_REVERSAL context.
2. However, in the very first few moments of the market opening, price action can be chaotic. The DetectorEngine spotted a fleeting bearish "inverted V-shape" pattern in this noise.
3. The AI was presented with two conflicting critical alerts: the sentinel's "BUY" signal and the detector's "SELL" signal. It defaulted to acting on the pattern detector, which was a mistake. The signal from a multi-day weekend gap is far more significant than a momentary M5 pattern.
4. Later, when the true bullish momentum appeared and the AI correctly identified a BUY signal, the system was already locked in the wrong SELL trade. The failsafe logic was too specific (only checking for "gapped weekend holdovers") and didn't apply to the newly opened trade, causing it to miss the correct entry.

The Solution: Signal Dominance Hierarchy & Failsafe Generalisation
Implement a strict "Signal Dominance Hierarchy" to resolve this and generalize the failsafe logic.

1. Absolute Priority for Sentinel Reversal: The Sentinel-Reversal signal is now the most dominant signal in the system. When it is active, the system will temporarily ignore all other pattern detectors during that first analysis cycle. This prevents noise from the market open from confusing the AI. The AI's sole mission will be to act on the gap momentum.
2. Generalizing the Failsafe: The failsafe logic was too narrow. It will be updated to a simpler, more powerful rule: If the AI issues a high-confidence reversal signal against any open trade (not just a weekend holdover), the system will close the current position to execute the higher-probability reversal.

These changes ensure that the powerful information from the weekend gap is acted upon correctly and that the system can flexibly reverse its position whenever the AI sees a high-confidence opportunity, preventing it from getting stuck in a bad trade.

##UPDATED LOG##
2025-10-06 10:55:12,779 - INFO - Successfully loaded optimized parameters: {'atr_sl_multiplier': 2.0, 'risk_reward_ratio': 1.2, 'quant_dev_score_threshold': 1.9}
2025-10-06 10:55:12,779 - INFO - Successfully loaded 6 plays from 'strategy_playbook.json'.
2025-10-06 10:55:12,779 - INFO - Successfully loaded 2 detector patterns.
2025-10-06 10:55:12,779 - INFO - Active trade #200930928 found. Executing Weekend Gap Sentinel immediately...
2025-10-06 10:55:12,779 - WARNING - Weekend Gap Sentinel: Detected trade #200930928 held over the weekend. Analysing gap...
2025-10-06 10:55:12,779 - WARNING - Weekend Gap Sentinel: Override: Gap caused a loss of 0.34R, exceeding the 0.33R threshold.
2025-10-06 10:55:12,779 - CRITICAL - WEEKEND GAP SENTINEL : Override: Gap caused a loss of 0.34R, exceeding the 0.33R threshold.. Closing Position #200930928 IMMEDIATELY.
2025-10-06 10:55:12,779 - INFO - Closing all 1 positions for SPX500. Reason: Weekend Gap Sentinel
2025-10-06 10:55:13,030 - INFO - SUCCESS: Position 200930928 closed.
2025-10-06 10:55:13,030 - INFO - Weekend Gap Sentinel execution finished. Re-checking trade status...
2025-10-06 10:55:13,030 - WARNING - Trade found at startup was confirmed CLOSED by the Weekend Gap Sentinel.
2025-10-06 10:55:13,030 - INFO - --- Starting Gemini Trader v9.44 for SPX500 ---
2025-10-06 10:55:13,030 - WARNING - Sentinel-Reversal signal detected: BULLISH_REVERSAL. Triggering immediate analysis for counter-trade.
2025-10-06 10:55:13,030 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 10:55:13,030 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 10:55:14,811 - INFO - V-Shape Detector: Bearish Inverted V-Shape signal detected.
2025-10-06 10:55:14,811 - INFO - V-Shape Detector: Bearish Inverted V-Shape signal detected.
2025-10-06 10:55:14,811 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BEAR'.
2025-10-06 10:55:14,811 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 10:55:16,218 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.7)
2025-10-06 10:55:16,218 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 10:55:18,030 - INFO - Senior Analyst AI M5 Execution Decision: SELL. Reason: CRITICAL PATTERN ALERT: BEARISH_V_REVERSAL takes precedence over all other signals. The M5 price action needs to be validated against the pivot price to confirm rejection strength.
2025-10-06 10:55:18,030 - INFO - Attempting to open SELL 0.02 lots of SPX500 @ 6731.18
2025-10-06 10:55:18,264 - INFO - SUCCESS: Order 200952638 sent. Finding position ID...
2025-10-06 10:55:19,265 - INFO - Position ID found: #200952638 (Linked to Order 200952638).
2025-10-06 10:55:19,265 - INFO - Initialization complete. Trader is running. Press Ctrl+C to exit.
2025-10-06 10:55:19,265 - INFO - Current Market Session: ACTIVE_TRADING (Risk: STANDARD) - Normal trading hours.
2025-10-06 11:00:04,329 - INFO - --- New M5 Bar [2025-10-06 14:00:00] ---
2025-10-06 11:00:04,329 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 11:00:04,329 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 11:00:07,602 - INFO - V-Shape Detector: Bullish V-Shape signal detected.
2025-10-06 11:00:07,602 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BULL'.
2025-10-06 11:00:07,602 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 11:00:09,336 - INFO - Junior Analyst AI M15 Strategy: UP (Conf: 0.6)
2025-10-06 11:00:09,336 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 11:00:11,102 - INFO - Senior Analyst AI M5 Execution Decision: BUY. Reason: The critical BULLISH_V_REVERSAL alert takes precedence. The M5 V-shape pattern, coupled with the M5 price trading above the EMA_9 (6731.119) and nearing the signal start price (6731.18), along with an elevated M5 RSI (66.19), strongly suggests an immediate upward reversal. This aligns with the existing M15 bullish bias and H1 bullish context.
2025-10-06 11:00:11,102 - INFO - High-confidence reversal signal 'BUY' received, but trade #200952638 is not a gapped weekend holdover. Holding position.
2025-10-06 11:02:36,138 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:02:41,139 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:02:46,141 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:02:51,142 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:02:56,143 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:03:01,144 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:03:06,146 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:03:16,148 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:03:21,149 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:03:56,156 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:04:01,158 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:04:06,159 - INFO - Trade #200952638 is in its grace period. Skipping advisory.
2025-10-06 11:05:01,172 - INFO - --- New M5 Bar [2025-10-06 14:05:00] ---
2025-10-06 11:05:01,172 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 11:05:01,172 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 11:05:02,488 - INFO - V-Shape Detector: Bullish V-Shape signal detected.
2025-10-06 11:05:02,488 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BULL'.
2025-10-06 11:05:02,488 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 11:05:04,754 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.7)
2025-10-06 11:05:04,754 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 11:05:06,411 - INFO - Senior Analyst AI M5 Execution Decision: BUY. Reason: CRITICAL BULLISH_V_REVERSAL pattern detected on M5, indicating a strong potential for immediate upward price movement following a liquidity hunt. The M5 RSI is at 100, further reinforcing the strength of this reversal signal. Despite the H1 bearish context, the critical M5 reversal pattern takes precedence.
2025-10-06 11:05:06,411 - INFO - High-confidence reversal signal 'BUY' received, but trade #200952638 is not a gapped weekend holdover. Holding position.
2025-10-06 11:10:01,489 - INFO - --- New M5 Bar [2025-10-06 14:10:00] ---
2025-10-06 11:10:01,489 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 11:10:01,489 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 11:10:02,559 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 11:10:04,419 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.7)
2025-10-06 11:10:04,419 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 11:10:06,340 - INFO - Senior Analyst AI M5 Execution Decision: HOLD. Reason: The M15 context shows EMA convergence and a neutral RSI, supporting the 'FLAT' bias. While the H1 context is bearish, there's no immediate signal for entry or exit. The active SELL trade at 6731.18 is currently showing a minor loss, but the lack of strong directional momentum across timeframes suggests waiting for clearer signals.
2025-10-06 11:15:01,406 - INFO - --- New M5 Bar [2025-10-06 14:15:00] ---
2025-10-06 11:15:01,406 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 11:15:01,406 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 11:15:02,415 - INFO - V-Shape Detector: Bullish V-Shape signal detected.
2025-10-06 11:15:02,415 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BULL'.
2025-10-06 11:15:02,415 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 11:15:04,134 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.7)
2025-10-06 11:15:04,134 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 11:15:06,056 - INFO - Senior Analyst AI M5 Execution Decision: BUY. Reason: The CRITICAL BULLISH_V_REVERSAL alert takes precedence. The M5 data supports a potential immediate long entry with the price trading above the M5 EMA_9 (6731.20) and M5 EMA_50 (6729.37). The M5 RSI_5 at 61.07 also indicates some bullish momentum.
2025-10-06 11:15:06,056 - INFO - High-confidence reversal signal 'BUY' received, but trade #200952638 is not a gapped weekend holdover. Holding position.


##LOG WITH FIX##
2025-10-06 11:16:26,793 - INFO - Successfully loaded optimized parameters: {'atr_sl_multiplier': 2.0, 'risk_reward_ratio': 1.2, 'quant_dev_score_threshold': 1.9}
2025-10-06 11:16:26,793 - INFO - Successfully loaded 6 plays from 'strategy_playbook.json'.
2025-10-06 11:16:26,793 - INFO - Successfully loaded 2 detector patterns.
2025-10-06 11:16:26,793 - INFO - Active trade #200952638 found. Executing Weekend Gap Sentinel immediately...
2025-10-06 11:16:26,793 - INFO - Weekend Gap Sentinel: Trade #200952638 is new, skipping gap check.
2025-10-06 11:16:26,793 - INFO - Weekend Gap Sentinel execution finished. Re-checking trade status...
2025-10-06 11:16:26,793 - WARNING - ACTIVE TRADE SYNCED: Position #200952638 remains active post-sentinel.
2025-10-06 11:16:26,793 - INFO - --- Starting Gemini Trader v9.45 for SPX500 ---
2025-10-06 11:16:26,793 - INFO - Initialization complete. Trader is running. Press Ctrl+C to exit.
2025-10-06 11:16:26,793 - INFO - Current Market Session: ACTIVE_TRADING (Risk: STANDARD) - Normal trading hours.
2025-10-06 11:20:01,847 - INFO - --- New M5 Bar [2025-10-06 14:20:00] ---
2025-10-06 11:20:01,847 - INFO - --- Running Full Analysis Cycle ---
2025-10-06 11:20:01,847 - INFO - Current Market Act: STEPS_PHASE
2025-10-06 11:20:03,613 - INFO - V-Shape Detector: Bullish V-Shape signal detected.
2025-10-06 11:20:03,613 - WARNING - DETECTOR MATCH! Triggered 'M5_V_SHAPE_BULL'.
2025-10-06 11:20:03,613 - INFO - Junior Analyst AI (M15 Strategist) analysing
2025-10-06 11:20:05,988 - INFO - Junior Analyst AI M15 Strategy: FLAT (Conf: 0.6)
2025-10-06 11:20:05,988 - INFO - Senior Analyst AI (M5 Execution) analysing
2025-10-06 11:20:07,692 - INFO - Senior Analyst AI M5 Execution Decision: BUY. Reason: CRITICAL BULLISH_V_REVERSAL pattern detected on M5, suggesting a sharp market reversal following a liquidity hunt. The signal suggests an immediate long entry.
2025-10-06 11:20:07,692 - CRITICAL - GENERAL FAILSAFE! High-confidence (0.80) reversal signal 'BUY' against active trade #200952638. Closing position to execute new trade.
2025-10-06 11:20:07,692 - INFO - Closing all 1 positions for SPX500. Reason: AI Reversal: BUY
2025-10-06 11:20:07,911 - INFO - SUCCESS: Position 200952638 closed.
2025-10-06 11:20:07,926 - INFO - Attempting to open BUY 0.02 lots of SPX500 @ 6731.74
2025-10-06 11:20:08,160 - INFO - SUCCESS: Order 200953762 sent. Finding position ID...
2025-10-06 11:20:09,162 - INFO - Position ID found: #200953762 (Linked to Order 200953762).