133 lines
4.5 KiB
Python
Executable File
133 lines
4.5 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Run market analysis and display results
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"""
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import sys
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import json
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import logging
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from pathlib import Path
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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# Load .env file
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from dotenv import load_dotenv
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load_dotenv(Path(__file__).parent.parent / '.env')
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from analysis.engine import MarketAnalysisEngine
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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def print_section(title: str, content: dict, indent: int = 0):
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"""Pretty print a section"""
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indent_str = " " * indent
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print(f"\n{indent_str}{'=' * 60}")
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print(f"{indent_str}{title}")
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print(f"{indent_str}{'=' * 60}")
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for key, value in content.items():
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if isinstance(value, dict):
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print(f"{indent_str}{key}:")
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for k, v in value.items():
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print(f"{indent_str} {k}: {v}")
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elif isinstance(value, list):
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print(f"{indent_str}{key}: {', '.join(map(str, value)) if value else '[]'}")
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else:
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print(f"{indent_str}{key}: {value}")
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def main():
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print("🚀 Starting Market Analysis Engine...")
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engine = MarketAnalysisEngine()
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# Check data availability
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print("\n📊 Checking data availability...")
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status = engine.check_data_availability()
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print(json.dumps(status, indent=2, ensure_ascii=False))
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# Perform analysis
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print("\n🔍 Performing market analysis...")
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analysis = engine.analyze_current_market(timeframe='5m')
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if 'error' in analysis:
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print(f"❌ Error: {analysis['error']}")
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return
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# Display results
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print_section("📈 MARKET OVERVIEW", {
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'Symbol': analysis['symbol'],
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'Timeframe': analysis['timeframe'],
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'Current Price': f"${analysis['current_price']:,.2f}",
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'Timestamp': analysis['timestamp'],
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})
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print_section("📊 TREND ANALYSIS", analysis['trend_analysis'])
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print_section("💰 SUPPORT & RESISTANCE", {
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'Nearest Support': analysis['support_resistance'].get('nearest_support'),
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'Nearest Resistance': analysis['support_resistance'].get('nearest_resistance'),
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'All Support': analysis['support_resistance'].get('support', []),
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'All Resistance': analysis['support_resistance'].get('resistance', []),
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})
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print_section("⚡ MOMENTUM", analysis['momentum'])
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print_section("📉 KEY INDICATORS", {
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'RSI': analysis['indicators']['rsi'],
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'MACD': analysis['indicators']['macd'],
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'MACD Histogram': analysis['indicators']['macd_hist'],
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'ADX': analysis['indicators']['adx'],
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'ATR': analysis['indicators']['atr'],
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'BB Width': analysis['indicators']['bb_width'],
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'Volume Ratio': analysis['indicators']['volume_ratio'],
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})
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# Order flow if available
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if 'orderflow' in analysis:
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print_section("💸 ORDER FLOW", {
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'Imbalance': f"{analysis['orderflow']['imbalance']['imbalance_pct']}%",
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'Status': analysis['orderflow']['imbalance']['status'],
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'Pressure': analysis['orderflow']['imbalance']['pressure'],
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'Strength': analysis['orderflow']['strength']['strength'],
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'Large Bids': analysis['orderflow']['large_orders']['large_bids_count'],
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'Large Asks': analysis['orderflow']['large_orders']['large_asks_count'],
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})
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print_section("🏦 LIQUIDITY", {
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'Spread': f"{analysis['orderflow']['liquidity']['spread_pct']:.4f}%",
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'Best Bid': f"${analysis['orderflow']['liquidity']['best_bid']:,.2f}",
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'Best Ask': f"${analysis['orderflow']['liquidity']['best_ask']:,.2f}",
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})
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# Breakout detection
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if analysis['breakout'].get('has_breakout'):
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print_section("🚨 BREAKOUT DETECTED", analysis['breakout'])
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elif 'approaching' in analysis['breakout']:
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print_section("⚠️ APPROACHING KEY LEVEL", analysis['breakout'])
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# Multi-timeframe
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print("\n\n🕐 MULTI-TIMEFRAME ANALYSIS")
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print("=" * 60)
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mtf = engine.get_multi_timeframe_analysis()
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for tf, data in mtf.items():
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print(f"\n{tf:>4}: {data['trend']:^6} | Strength: {data['strength']:^8} | RSI: {data['rsi']:>5.1f} | ADX: {data['adx']:>5.1f}")
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# LLM Context
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print("\n\n🤖 LLM CONTEXT (Simplified)")
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print("=" * 60)
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llm_context = engine.get_llm_context(format='simplified')
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print(json.dumps(llm_context, indent=2, ensure_ascii=False))
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print("\n✅ Analysis complete!")
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if __name__ == "__main__":
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main()
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