""" Улучшенный спекулятивный анализ ВСЕХ тикеров MOEX (22.06.2026) Использует T-Bank API (live prices + orderbook) + DB (история) """ import sys, os, json sys.path.insert(0, os.path.dirname(__file__)) import pandas as pd import numpy as np from datetime import datetime from src.db.connection import fetch_ohlcv_combined, get_connection from src.indicators.calculations import calc_all_indicators from src.analysis.tech_analysis import ( detect_candlestick_patterns, detect_vsa_signals, find_key_levels, determine_trend, wyckoff_phase, generate_trade_signal, ) from src.api.tbank import get_last_price, get_current_candle, get_order_book, analyze_order_book, _find_figi MOEX_TICKERS = [ 'SBER', 'GAZP', 'LKOH', 'ROSN', 'NVTK', 'MGNT', 'TATN', 'SNGS', 'PLZL', 'PHOR', 'NLMK', 'CHMF', 'GMKN', 'ALRS', 'MTSS', 'VTBR', 'MOEX', 'FIVE', 'AFLT', 'POLY', 'RUAL', 'SELG', 'IRAO', 'HYDR', 'MAGN', 'RASP', 'NMTP', 'FESH', 'CBOM', 'TCSG', 'VKCO', 'YNDX', 'OZON', 'X5', 'ASTR', ] def table_exists(ticker, tf): conn = get_connection() try: cursor = conn.cursor() cursor.execute(f"SHOW TABLES LIKE '{ticker}_{tf}'") return cursor.fetchone() is not None finally: conn.close() def enhanced_analyze(ticker): """Улучшенный анализ с T-Bank API и OrderBook.""" print(f"\n=== {ticker} ===") result = { 'ticker': ticker, 'error': None, 'has_db_data': False, 'tbank_price': None, 'trend_w1': '—', 'trend_d1': '—', 'trend_h1': '—', 'wyckoff_phase': '—', 'wyckoff_direction': '—', 'signal': 'HOLD', 'confidence': 0, 'entry': None, 'sl': None, 'tp': None, 'atr': None, 'reasons': [], 'levels_support': [], 'levels_resistance': [], 'orderbook': None, } # 1. T-Bank API: current price + orderbook tbank_price = None ob = None ob_analysis = None try: tbank_price = get_last_price(ticker) if tbank_price: result['tbank_price'] = tbank_price print(f" T-Bank price: {tbank_price:.2f}") except Exception as e: print(f" T-Bank price error: {e}") try: ob = get_order_book(ticker, depth=20) ob_analysis = analyze_order_book(ob, large_order_threshold=3000) result['orderbook'] = { 'bid': ob.best_bid, 'ask': ob.best_ask, 'spread_pct': round(ob.spread_pct, 3), 'imbalance': round(ob.imbalance_ratio, 2), 'verdict': ob_analysis.verdict, } print(f" OrderBook: bid={ob.best_bid} ask={ob.best_ask} " f"spread={ob.spread_pct:.3f}% imbalance={ob.imbalance_ratio:.2f} → {ob_analysis.verdict}") except Exception as e: print(f" OrderBook error: {e}") # Текущие свечи из T-Bank API tbank_candles = {} for tf_name in ['W1', 'D1', 'H1']: try: cur_candle = get_current_candle(ticker, tf_name) if cur_candle: tbank_candles[tf_name] = cur_candle print(f" Текущая {tf_name}: {cur_candle['Date']} " f"O={cur_candle['Open']:.2f} H={cur_candle['High']:.2f} " f"L={cur_candle['Low']:.2f} C={cur_candle['Close']:.2f}") except Exception: pass result['tbank_candles'] = tbank_candles # 2. DB data has_h1 = table_exists(ticker, 'H1') has_d1 = table_exists(ticker, 'D1') has_w1 = table_exists(ticker, 'W1') result['has_db_data'] = has_h1 or has_d1 or has_w1 current_price = tbank_price if has_h1 or has_d1: try: df_h1 = fetch_ohlcv_combined(ticker, 'H1', limit=200) if has_h1 else pd.DataFrame() df_d1 = fetch_ohlcv_combined(ticker, 'D1', limit=200) if has_d1 else pd.DataFrame() df_w1 = fetch_ohlcv_combined(ticker, 'W1', limit=100) if has_w1 else pd.DataFrame() print(f" DB: H1={len(df_h1)} D1={len(df_d1)} W1={len(df_w1)}") if current_price is None: if len(df_h1) > 0: current_price = float(df_h1['Close'].iloc[-1]) elif len(df_d1) > 0: current_price = float(df_d1['Close'].iloc[-1]) if len(df_h1) >= 20: df_h1 = calc_all_indicators(df_h1) df_h1 = detect_candlestick_patterns(df_h1) df_h1 = detect_vsa_signals(df_h1) if len(df_d1) >= 20: df_d1 = calc_all_indicators(df_d1) df_d1 = detect_candlestick_patterns(df_d1) df_d1 = detect_vsa_signals(df_d1) if len(df_w1) >= 20: df_w1 = calc_all_indicators(df_w1) # Trend analysis if len(df_w1) >= 20: trend_w1, adx_w1 = determine_trend(df_w1) result['trend_w1'] = trend_w1 result['adx_w1'] = round(adx_w1, 1) if len(df_d1) >= 20: trend_d1, adx_d1 = determine_trend(df_d1) result['trend_d1'] = trend_d1 result['adx_d1'] = round(adx_d1, 1) if len(df_h1) >= 20: trend_h1, adx_h1 = determine_trend(df_h1) result['trend_h1'] = trend_h1 result['adx_h1'] = round(adx_h1, 1) # Wyckoff if len(df_w1) >= 20: wyckoff = wyckoff_phase(df_w1) result['wyckoff_phase'] = wyckoff['phase'] result['wyckoff_direction'] = wyckoff.get('direction', '—') result['wyckoff_tr_low'] = wyckoff.get('tr_low') result['wyckoff_tr_high'] = wyckoff.get('tr_high') # Levels levels_h1 = find_key_levels(df_h1) if len(df_h1) >= 20 else {'support': [], 'resistance': []} levels_d1 = find_key_levels(df_d1) if len(df_d1) >= 20 else {'support': [], 'resistance': []} all_levels = { 'support': list(set(levels_h1.get('support', []) + levels_d1.get('support', []))), 'resistance': list(set(levels_h1.get('resistance', []) + levels_d1.get('resistance', []))), } if current_price: all_levels['support'] = sorted([x for x in all_levels['support'] if x < current_price], reverse=True)[:5] all_levels['resistance'] = sorted([x for x in all_levels['resistance'] if x > current_price])[:5] result['levels_support'] = all_levels['support'] result['levels_resistance'] = all_levels['resistance'] # Signal if current_price and len(df_h1) >= 20: signal = generate_trade_signal( trend_w1=trend_w1 if 'trend_w1' in result and result['trend_w1'] != '—' else 'sideways', trend_d1=trend_d1 if 'trend_d1' in result else 'sideways', trend_h1=trend_h1 if 'trend_h1' in result else 'sideways', phase=result['wyckoff_phase'], levels=all_levels, df_d1=df_d1, df_h1=df_h1, current_price=current_price, ) result['signal'] = signal['signal'] result['confidence'] = signal['confidence'] result['entry'] = signal['entry'] result['sl'] = signal['sl'] result['tp'] = signal['tp'] result['reasons'] = signal['reason'] result['atr'] = signal.get('atr', 0) # Last candle info if len(df_h1) > 0: last_h1 = df_h1.iloc[-1] result['last_h1'] = { 'date': str(last_h1.get('Date', '')), 'time': str(last_h1.get('Time', '')), 'open': float(last_h1['Open']), 'high': float(last_h1['High']), 'low': float(last_h1['Low']), 'close': float(last_h1['Close']), 'volume': int(last_h1.get('Volume', 0)), 'vsa': str(last_h1.get('VSA_signal', '')), 'candle': str(last_h1.get('candle_pattern', '')), } if len(df_d1) > 0: last_d1 = df_d1.iloc[-1] result['last_d1'] = { 'date': str(last_d1.get('Date', '')), 'open': float(last_d1['Open']), 'high': float(last_d1['High']), 'low': float(last_d1['Low']), 'close': float(last_d1['Close']), 'volume': int(last_d1.get('Volume', 0)), 'vsa': str(last_d1.get('VSA_signal', '')), 'candle': str(last_d1.get('candle_pattern', '')), } except Exception as e: result['error'] = str(e) print(f" DB error: {e}") # Finally add OB bias boost if result['orderbook'] and result['signal'] != 'HOLD': ob_verdict = result['orderbook']['verdict'] if 'БЫЧИЙ' in ob_verdict and result['signal'] == 'BUY': result['confidence'] = min(90, result['confidence'] + 10) result['reasons'] = str(result.get('reasons', '')) + '; OrderBook бычий bias +10%' elif 'МЕДВЕЖИЙ' in ob_verdict and result['signal'] == 'SELL': result['confidence'] = min(90, result['confidence'] + 10) result['reasons'] = str(result.get('reasons', '')) + '; OrderBook медвежий bias +10%' elif 'БЫЧИЙ' in ob_verdict and result['signal'] == 'SELL': result['reasons'] = str(result.get('reasons', '')) + '; ⚠ OrderBook бычий bias противоречит SELL' elif 'МЕДВЕЖИЙ' in ob_verdict and result['signal'] == 'BUY': result['reasons'] = str(result.get('reasons', '')) + '; ⚠ OrderBook медвежий bias противоречит BUY' print(f" → {result['signal']} (conf: {result['confidence']}%) | W1:{result['trend_w1']} D1:{result['trend_d1']} H1:{result['trend_h1']}") return result def main(): print("=" * 70) print("УЛУЧШЕННЫЙ ТЕХНИЧЕСКИЙ АНАЛИЗ MOEX (T-Bank API + DB)") print(f"Дата: {datetime.now().strftime('%Y-%m-%d %H:%M')}") print("=" * 70) results = [] for ticker in MOEX_TICKERS: result = enhanced_analyze(ticker) results.append(result) # Summary print(f"\n{'='*70}") print("СВОДКА РЕЗУЛЬТАТОВ") print(f"{'='*70}") signals = {'BUY': [], 'SELL': [], 'HOLD': []} for r in results: sig = r.get('signal', 'HOLD') if sig in signals: signals[sig].append(r) print(f"BUY: {len(signals['BUY'])} | SELL: {len(signals['SELL'])} | HOLD: {len(signals['HOLD'])}") print() for sig_type in ['BUY', 'SELL']: tickers_sig = sorted(signals[sig_type], key=lambda x: x.get('confidence', 0), reverse=True) if tickers_sig: print(f"--- {sig_type} Signals ---") for r in tickers_sig: conf = r.get('confidence', 0) price = r.get('tbank_price') or r.get('entry', 0) ob_info = '' if r.get('orderbook'): obv = r['orderbook']['verdict'] ob_info = f' | OB: {obv}' wyck = r.get('wyckoff_phase', '') print(f" {r['ticker']:6s} conf={conf:3.0f}% price={price:>8.2f} | {wyck[:35]}{ob_info}") # Save output = { 'timestamp': datetime.now().isoformat(), 'results': results, } with open('/tmp/moex_enhanced_results.json', 'w') as f: json.dump(output, f, indent=2, ensure_ascii=False, default=str) print(f"\nРезультаты сохранены в /tmp/moex_enhanced_results.json") return results if __name__ == '__main__': main()