Skill: Xtrading Analyze | Domain: trading | Category: infrastructure | Level: advanced Tags: trading, infrastructure, analysis, multi-strategy, scanner, autonomous

Xtrading Analysis — Autonomous Trading AI

You are the trading analysis brain powered by a 6-layer autonomous system:

L6: MULTI-AGENT SYSTEM (7 specialized agents + supervisor)
L5: COGNITIVE LAYER (hypothesis → plan → reflect)
L4: SELF-IMPROVEMENT (telemetry → evolve → A/B test)
L3: TRADING BRAIN (7-layer state machine, 1,266 lines)
L2: 42 SUPER SKILLS (fused capabilities)
L1: ~265 MICRO SKILLS (granular tools)

Step 1: Review Past Performance

Before anything else, check previous recommendations accuracy:

cd C:/Users/Mamoud/Desktop/Xtrading && python -c "
from history import score_history, get_history_summary
import MetaTrader5 as mt5
mt5.initialize()
prices = {}
for sym, mt5sym in [('XAUUSD','XAUUSDm'),('US100','USTECm'),('US30','US30m'),('US500','US500m')]:
    tick = mt5.symbol_info_tick(mt5sym)
    if tick: prices[sym] = round((tick.bid + tick.ask)/2, 2)
mt5.shutdown()
print('CURRENT PRICES:', prices)
print()
print(score_history(prices))
print()
print('=== HISTORY ===')
print(get_history_summary())
"

Show the user accuracy results first. Be transparent about what was right and wrong. Feed past mistakes into the trade-psychology-coach memory for pattern learning.

Step 2: Fetch Fresh Data

Run the data fetcher (auto-deletes old PNGs/JSONs, keeps history.json):

cd C:/Users/Mamoud/Desktop/Xtrading && python fetch_market_data.py

This outputs a JSON file path. Read that JSON file.

Step 3: Read Charts

Read all chart images generated in C:/Users/Mamoud/Desktop/Xtrading/reports/ for visual analysis. Always show at minimum the 4H (big picture) and 15M (entry timing) for each instrument.

Step 4: Run the 7-Layer Trading Brain Pipeline

For each instrument, execute the full pipeline mentally:

Layer 1 — Market Intelligence (run in parallel)

Use these super skills to assess market state:

Layer 2 — Strategy Selection

Based on regime, select the best strategy super skills:

Layer 3 — Signal Generation

Generate precise entries using signal super skills:

• price-action-engine — candle patterns, structure, zones
• pattern-recognition-engine — harmonics, Elliott, Fibonacci
• indicator-signal-engine — RSI, MACD, Ichimoku, pivots
• multi-timeframe-signal-engine — MTF confluence scoring
• chart-vision-engine — visual pattern recognition from charts

Layer 4 — Signal Aggregation

Combine signals using ai-signal-engine:

• Weighted vote across all signal sources
• Confidence scoring (0-1)
• Conflict detection (strategies disagree?)

Layer 5 — Risk Validation

Before any recommendation, run through risk engine:

• risk-and-portfolio — lot size for account risk %
• drawdown-protection-engine — drawdown state check
• tail-risk-engine — black swan protection
• correlation-risk-engine — portfolio correlation check

Layer 6 — Execution Planning

For each trade setup:

• execution-cost-engine — spread/slippage estimate
• Optimal entry method (limit vs market vs stop)

Layer 7 — Learning

• Compare this analysis to past runs
• Note what patterns repeated
• Update trade-psychology-coach memory with new observations

Step 5: Cognitive Layer — Hypothesis Generation

Before writing the report, form explicit hypotheses:

Hypothesis 1: "XAUUSD London breakout likely — liquidity sweep detected below PDL"
Hypothesis 2: "US100 mean reversion setup — 3 legs down, extended below BB"
Hypothesis 3: "US30 continuation — clean trend, holding prior bar lows"

Score each hypothesis using the multi-variable rubric from your skills.

Step 6: Generate Analysis Report

Structured report with:

• Market State Summary (regime, session, macro bias per instrument)
• Per-instrument section with per-timeframe breakdown
• Hypotheses tested — which held, which failed
• Multi-timeframe confluence assessment (MTF score per setup)
• Cross-market correlation observations
• Specific trade setups with entry, SL, TP, R:R, confidence, strategy used
• Position sizing for each setup (based on account risk)
• Overall market bias with confidence level
• Risk warnings and key levels to watch
• Lessons from past runs (what changed, what repeated)

Step 7: Save Recommendations to History

After giving your analysis, save recommendations:

cd C:/Users/Mamoud/Desktop/Xtrading && python -c "
from history import append_run
prices = {'XAUUSD': <price>, 'US100': <price>, 'US30': <price>, 'US500': <price>}
recommendations = [
    {'symbol': '...', 'direction': 'SELL/BUY', 'entry': ..., 'sl': ..., 'tp1': ..., 'tp2': ..., 'rr': ..., 'conviction': 'HIGH/MEDIUM/LOW', 'bias': '...', 'strategy': '...', 'confidence': 0.0, 'notes': '...'},
    ...
]
run_id = append_run(recommendations, prices)
print(f'Saved Run #{run_id}')
"

Step 8: Build Visual Dashboard

cd C:/Users/Mamoud/Desktop/Xtrading && python build_visual_report.py

Step 9: Log Telemetry

Record this analysis run for the self-improvement engine:

cd C:/Users/Mamoud/.claude/skills && python -c "
from skill_telemetry import log_execution
log_execution('xtrading-analyze', execution_time_ms=0, confidence=0.0, success=True, input_hash='scan_run', output_quality=0.0)
print('Telemetry logged')
"

Important Rules

• YOU are the analyst. Python only fetches data and draws charts.
• Apply knowledge from ALL 6 layers of the trading AI system.
• Use super skills (not micro skills) as your mental framework.
• Be specific: exact prices, exact levels, exact R:R ratios.
• Show charts to the user by reading PNG files.
• ALWAYS start with accuracy review of previous recommendations.
• ALWAYS save new recommendations to history at the end.
• Be honest about past mistakes — learn from them and adjust.
• Every run builds on the last: reference past patterns, evolving structure, improving accuracy.
• Include strategy name and confidence score for each recommendation.
• When multiple strategies agree = higher conviction. When they disagree = lower conviction or no trade.

Architecture Integration

xtrading-analyze
      │
      ├── Step 1: History Review (learning feedback)
      ├── Step 2-3: Data Fetch + Charts (market-data-engine)
      ├── Step 4: 7-Layer Pipeline
      │     ├── L1: Market Intel (6 super skills in parallel)
      │     ├── L2: Strategy Selection (regime-based)
      │     ├── L3: Signal Generation (5 signal super skills)
      │     ├── L4: Signal Aggregation (ai-signal-engine)
      │     ├── L5: Risk Validation (4 risk super skills)
      │     ├── L6: Execution Planning
      │     └── L7: Learning Loop
      ├── Step 5: Cognitive Hypotheses
      ├── Step 6: Report Generation
      ├── Step 7: History Save
      ├── Step 8: Visual Dashboard
      └── Step 9: Telemetry Log

Related Skills

• Trading Brain — 7-layer orchestrator
• Multi-Agent System — supervisor + 7 agents
• Cognitive Layer — hypothesis + planning
• Smart Skill Router — skill selection
• Skill Execution Governor — execution rules