""" Полный спекулятивный технический анализ ВСЕХ тикеров из базы данных MySQL. Источники данных: - MySQL (nlbotinterface.ru:3306 / bitcoin_tickers) — история OHLCV H1/D1/W1 - T-Bank Invest API (real, только чтение) — текущие цены + биржевой стакан Для каждого тикера: 1. Загрузка H1/D1/W1 из БД 2. Расчёт индикаторов (EMA/SMA/MACD/RSI/Stoch/BB/ATR/ADX/CCI/OBV) 3. Свечные паттерны + VSA-сигналы 4. Multi-Timeframe тренды (W1=50% / D1=30% / H1=20%) 5. Фаза Вайкоффа (Accumulation/Markdown/Markup/Distribution) 6. Ключевые уровни S/R 7. T-Bank OrderBook (дисбаланс bid/ask, стены) — для MOEX-тикеров 8. Генерация сигнала BUY/SELL/HOLD с confidence-оценкой Запуск: python _analyze_all_db.py """ import sys import os import json import warnings from datetime import datetime sys.path.insert(0, os.path.dirname(__file__)) warnings.filterwarnings("ignore") import pandas as pd import numpy as np from src.db.connection import fetch_ohlcv, get_available_tickers 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_order_book, analyze_order_book, set_tbank_env, _find_figi, ) # Тикеры, которые относятся к MOEX (остальные в БД — крипто/FOREX) MOEX_TICKERS_SET = { 'SBER', 'GAZP', 'LKOH', 'ROSN', 'NVTK', 'MGNT', 'TATN', 'SNGS', 'SNGSP', 'PLZL', 'PHOR', 'NLMK', 'CHMF', 'GMKN', 'ALRS', 'MTSS', 'VTBR', 'MOEX', 'FIVE', 'X5', 'AFLT', 'POLY', 'RUAL', 'SELG', 'IRAO', 'HYDR', 'MAGN', 'RASP', 'NMTP', 'FESH', 'CBOM', 'TCSG', 'VKCO', 'YNDX', 'OZON', 'ASTR', # Привилегированные акции 'SBERP', 'TATNP', } def classify_ticker(ticker: str) -> str: """Классификация тикера: MOEX / CRYPTO / FOREX / UNKNOWN.""" t = ticker.upper() if t in MOEX_TICKERS_SET: return 'MOEX' if 'BITCOIN' in t or 'BTC' in t or 'ETH' in t: return 'CRYPTO' if 'USD' in t or 'EUR' in t or 'RUB' in t or 'JPY' in t: return 'FOREX' return 'UNKNOWN' def safe_float(val, default=0.0): """Безопасное преобразование в float.""" try: if val is None or (isinstance(val, float) and np.isnan(val)): return default return float(val) except (TypeError, ValueError): return default def analyze_ticker(ticker: str, use_real_api: bool = True) -> dict: """ Полный спекулятивный анализ одного тикера. Args: ticker: тикер. use_real_api: использовать real T-Bank API для актуальных цен/стакана. Returns: словарь с результатами анализа. """ result = { 'ticker': ticker, 'class': classify_ticker(ticker), 'error': None, 'has_db_data': False, 'tbank_price': None, 'trend_w1': '—', 'trend_d1': '—', 'trend_h1': '—', 'adx_w1': None, 'adx_d1': None, 'adx_h1': None, 'rsi_d1': None, 'rsi_h1': None, 'stoch_d1': None, 'stoch_h1': None, 'cci_d1': None, 'cci_h1': None, 'macd_d1': None, 'wyckoff_phase': '—', 'wyckoff_direction': '—', 'wyckoff_events': [], 'wyckoff_tr_low': None, 'wyckoff_tr_high': None, 'signal': 'HOLD', 'confidence': 0, 'entry': None, 'sl': None, 'tp': None, 'atr': None, 'reasons': '', 'levels_support': [], 'levels_resistance': [], 'last_h1': None, 'last_d1': None, 'last_w1': None, 'orderbook': None, 'candle_d1': '', 'candle_h1': '', 'vsa_d1': '', 'vsa_h1': '', } print(f"\n=== {ticker} ({result['class']}) ===") # ── 2. Данные из БД (загружаем ПЕРВЫМИ — эталон цены) ── try: df_h1 = fetch_ohlcv(ticker, 'H1', limit=300) df_d1 = fetch_ohlcv(ticker, 'D1', limit=300) df_w1 = fetch_ohlcv(ticker, 'W1', limit=150) except Exception as e: result['error'] = f"DB error: {e}" print(f" DB error: {e}") return result has_h1 = len(df_h1) > 0 has_d1 = len(df_d1) > 0 has_w1 = len(df_w1) > 0 result['has_db_data'] = has_h1 or has_d1 or has_w1 print(f" DB rows: H1={len(df_h1)} D1={len(df_d1)} W1={len(df_w1)}") if not (has_h1 or has_d1 or has_w1): result['error'] = 'Нет данных в БД' return result # Эталонная цена из БД (последняя H1, затем D1, затем W1) db_price = None if has_h1: db_price = safe_float(df_h1['Close'].iloc[-1]) elif has_d1: db_price = safe_float(df_d1['Close'].iloc[-1]) elif has_w1: db_price = safe_float(df_w1['Close'].iloc[-1]) # ── 1. T-Bank API: текущая цена + стакан (только для MOEX) ── # ВАЖНО: sanity-check цены T-Bank vs БД. Если расхождение > 25% — # считаем FIGI подозрительным (возможно указывает на другой инструмент), # отбрасываем T-Bank цену и стакан, используем цену из БД. tbank_price = None ob_info = None has_figi = False figi_suspicious = False try: _find_figi(ticker) has_figi = True except ValueError: has_figi = False if has_figi: try: tbank_price = get_last_price(ticker) if tbank_price: # Sanity-check vs БД цену if db_price and db_price > 0: deviation = abs(tbank_price - db_price) / db_price if deviation > 0.25: print(f" ⚠ T-Bank price {tbank_price:.4f} отклоняется от БД {db_price:.4f} " f"на {deviation*100:.1f}% — FIGI подозрительный, цена/стакан отброшены") figi_suspicious = True result['figi_warning'] = ( f"T-Bank цена {tbank_price:.4f} расходится с БД {db_price:.4f} " f"на {deviation*100:.1f}% — возможно неверный FIGI" ) tbank_price = None else: result['tbank_price'] = round(tbank_price, 4) print(f" T-Bank price: {tbank_price:.4f} (отклонение от БД: {deviation*100:.1f}%)") except Exception as e: print(f" T-Bank price error: {e}") # Стакан — только если FIGI не подозрительный if not figi_suspicious: try: ob = get_order_book(ticker, depth=20) ob_analysis = analyze_order_book(ob, large_order_threshold=3000) ob_info = { 'bid': round(ob.best_bid, 4), 'ask': round(ob.best_ask, 4), 'spread_pct': round(ob.spread_pct, 3), 'imbalance': round(ob.imbalance_ratio, 2), 'total_bid_vol': ob.total_bid_volume, 'total_ask_vol': ob.total_ask_volume, 'verdict': ob_analysis.verdict, 'bid_walls': [(round(p, 2), q) for p, q in ob_analysis.bid_walls[:3]], 'ask_walls': [(round(p, 2), q) for p, q in ob_analysis.ask_walls[:3]], } result['orderbook'] = ob_info print(f" OrderBook: bid={ob.best_bid} ask={ob.best_ask} " f"spread={ob.spread_pct:.3f}% imb={ob.imbalance_ratio:.2f} " f"→ {ob_analysis.verdict}") except Exception as e: print(f" OrderBook error: {e}") # Текущая цена: приоритет T-Bank (если валидна), затем БД current_price = tbank_price if tbank_price else db_price if current_price is None or current_price <= 0: result['error'] = 'Не удалось определить текущую цену' return result # ── 3. Расчёт индикаторов ── if has_h1 and 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 has_d1 and 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 has_w1 and len(df_w1) >= 20: df_w1 = calc_all_indicators(df_w1) # ── 4. Тренды (Multi-Timeframe) ── trend_w1, adx_w1 = 'sideways', 0.0 trend_d1, adx_d1 = 'sideways', 0.0 trend_h1, adx_h1 = 'sideways', 0.0 if has_w1 and len(df_w1) >= 20: trend_w1, adx_w1 = determine_trend(df_w1) result['trend_w1'] = trend_w1 result['adx_w1'] = round(safe_float(adx_w1), 1) if has_d1 and len(df_d1) >= 20: trend_d1, adx_d1 = determine_trend(df_d1) result['trend_d1'] = trend_d1 result['adx_d1'] = round(safe_float(adx_d1), 1) if has_h1 and len(df_h1) >= 20: trend_h1, adx_h1 = determine_trend(df_h1) result['trend_h1'] = trend_h1 result['adx_h1'] = round(safe_float(adx_h1), 1) # ── 5. Осцилляторы (RSI, Stoch, CCI, MACD) ── if has_d1 and len(df_d1) >= 20: last_d1 = df_d1.iloc[-1] result['rsi_d1'] = round(safe_float(last_d1.get('RSI_14')), 1) if 'RSI_14' in df_d1 else None result['stoch_d1'] = round(safe_float(last_d1.get('Stoch_K')), 1) if 'Stoch_K' in df_d1 else None result['cci_d1'] = round(safe_float(last_d1.get('CCI_20')), 1) if 'CCI_20' in df_d1 else None if 'MACD_line' in df_d1: result['macd_d1'] = round(safe_float(last_d1.get('MACD_line')), 3) result['candle_d1'] = str(last_d1.get('candle_pattern', '')) result['vsa_d1'] = str(last_d1.get('VSA_signal', '')) if has_h1 and len(df_h1) >= 20: last_h1 = df_h1.iloc[-1] result['rsi_h1'] = round(safe_float(last_h1.get('RSI_14')), 1) if 'RSI_14' in df_h1 else None result['stoch_h1'] = round(safe_float(last_h1.get('Stoch_K')), 1) if 'Stoch_K' in df_h1 else None result['cci_h1'] = round(safe_float(last_h1.get('CCI_20')), 1) if 'CCI_20' in df_h1 else None result['candle_h1'] = str(last_h1.get('candle_pattern', '')) result['vsa_h1'] = str(last_h1.get('VSA_signal', '')) # ── 6. Фаза Вайкоффа (на W1) ── if has_w1 and len(df_w1) >= 20: wyck = wyckoff_phase(df_w1) result['wyckoff_phase'] = wyck['phase'] result['wyckoff_direction'] = wyck.get('direction', '—') result['wyckoff_events'] = wyck.get('events', []) result['wyckoff_tr_low'] = wyck.get('tr_low') result['wyckoff_tr_high'] = wyck.get('tr_high') # ── 7. Ключевые уровни S/R ── levels_h1 = find_key_levels(df_h1) if has_h1 and len(df_h1) >= 20 else {'support': [], 'resistance': []} levels_d1 = find_key_levels(df_d1) if has_d1 and len(df_d1) >= 20 else {'support': [], 'resistance': []} all_support = list(set(levels_h1.get('support', []) + levels_d1.get('support', []))) all_resistance = list(set(levels_h1.get('resistance', []) + levels_d1.get('resistance', []))) if current_price: all_support = sorted([x for x in all_support if x < current_price], reverse=True)[:5] all_resistance = sorted([x for x in all_resistance if x > current_price])[:5] result['levels_support'] = [round(x, 2) for x in all_support] result['levels_resistance'] = [round(x, 2) for x in all_resistance] # ── 8. Последние свечи ── if has_h1 and len(df_h1) > 0: last = df_h1.iloc[-1] result['last_h1'] = { 'date': str(last.get('Date', '')), 'time': str(last.get('Time', '')), 'open': round(safe_float(last['Open']), 4), 'high': round(safe_float(last['High']), 4), 'low': round(safe_float(last['Low']), 4), 'close': round(safe_float(last['Close']), 4), 'volume': int(safe_float(last.get('Volume', 0))), } if has_d1 and len(df_d1) > 0: last = df_d1.iloc[-1] result['last_d1'] = { 'date': str(last.get('Date', '')), 'open': round(safe_float(last['Open']), 4), 'high': round(safe_float(last['High']), 4), 'low': round(safe_float(last['Low']), 4), 'close': round(safe_float(last['Close']), 4), 'volume': int(safe_float(last.get('Volume', 0))), } if has_w1 and len(df_w1) > 0: last = df_w1.iloc[-1] result['last_w1'] = { 'date': str(last.get('Date', '')), 'open': round(safe_float(last['Open']), 4), 'high': round(safe_float(last['High']), 4), 'low': round(safe_float(last['Low']), 4), 'close': round(safe_float(last['Close']), 4), 'volume': int(safe_float(last.get('Volume', 0))), } # ── 9. Генерация сигнала ── # Multi-Timeframe: РЕШЕНИЕ на W1+D1, H1 — подтверждение + уточнение TP/SL. # levels_h1 — для SL (точный вход), levels_d1 — для TP (цель свинга). if current_price and (has_h1 or has_d1): # Если нет H1, используем D1 как замену df_h1_for_signal = df_h1 if has_h1 and len(df_h1) >= 20 else df_d1 df_d1_for_signal = df_d1 if has_d1 and len(df_d1) >= 20 else df_h1 try: signal = generate_trade_signal( trend_w1=trend_w1, trend_d1=trend_d1, trend_h1=trend_h1, phase=result['wyckoff_phase'], levels={'support': all_support, 'resistance': all_resistance}, df_d1=df_d1_for_signal, df_h1=df_h1_for_signal, current_price=current_price, levels_h1=levels_h1, levels_d1=levels_d1, ) 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) except Exception as e: result['error'] = f"Signal error: {e}" print(f" Signal error: {e}") # ── 10. Корректировка confidence по стакану ── if ob_info and result['signal'] != 'HOLD': ob_verdict = ob_info['verdict'] reasons = str(result.get('reasons', '')) if 'БЫЧИЙ' in ob_verdict and result['signal'] == 'BUY': result['confidence'] = min(90, result['confidence'] + 10) result['reasons'] = reasons + '; OrderBook бычий bias +10%' elif 'МЕДВЕЖИЙ' in ob_verdict and result['signal'] == 'SELL': result['confidence'] = min(90, result['confidence'] + 10) result['reasons'] = reasons + '; OrderBook медвежий bias +10%' elif 'БЫЧИЙ' in ob_verdict and result['signal'] == 'SELL': result['confidence'] = max(0, result['confidence'] - 10) result['reasons'] = reasons + '; ⚠ OrderBook бычий bias противоречит SELL (-10%)' elif 'МЕДВЕЖИЙ' in ob_verdict and result['signal'] == 'BUY': result['confidence'] = max(0, result['confidence'] - 10) result['reasons'] = reasons + '; ⚠ OrderBook медвежий bias противоречит BUY (-10%)' # ── 11. RR (reward:risk) ── if result['signal'] != 'HOLD' and result['entry'] and result['sl'] and result['tp']: risk = abs(result['entry'] - result['sl']) reward = abs(result['tp'] - result['entry']) result['rr'] = round(reward / risk, 2) if risk > 0 else None else: result['rr'] = None # ── 12. RR-фильтр (финальный guard): если RR < 2, confidence < 50% ── # Применяется ПОСЛЕ корректировки по стакану, чтобы bid/ask-дисбаланс # не мог "восстановить" confidence выше 50% для экономически невыгодных # сделок (RR < 2 — риск больше половины потенциальной прибыли). if (result['rr'] is not None and result['rr'] < 2.0 and result['confidence'] >= 50): result['confidence'] = 49 reasons = str(result.get('reasons', '')) if 'RR<2' not in reasons: result['reasons'] = reasons + '; ⚠ RR<2 → conf ограничен 49% (финальный guard)' sig_str = result['signal'] conf = result['confidence'] print(f" → {sig_str} (conf: {conf}%) | W1:{result['trend_w1']} D1:{result['trend_d1']} H1:{result['trend_h1']}") if result['wyckoff_phase'] != '—': print(f" Wyckoff: {result['wyckoff_phase']}") if result['rr']: print(f" Entry={result['entry']} SL={result['sl']} TP={result['tp']} RR=1:{result['rr']}") return result def main(): print("=" * 72) print("ПОЛНЫЙ СПЕКУЛЯТИВНЫЙ АНАЛИЗ ВСЕХ ТИКЕРОВ ИЗ БД (MySQL + T-Bank API)") print(f"Дата: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print("=" * 72) # Переключаемся на real API для актуальных рыночных данных (только чтение) print("\nПереключение T-Bank API → real (только чтение цен/стакана)...") try: set_tbank_env('real') except Exception as e: print(f" Не удалось переключиться на real: {e}. Используется sandbox.") # Получаем список тикеров из БД print("\nПолучение списка тикеров из БД...") tickers = get_available_tickers() print(f"Найдено тикеров в БД: {len(tickers)}") for t in tickers: print(f" {t:12s} → {classify_ticker(t)}") # Анализ каждого тикера results = [] for ticker in tickers: try: r = analyze_ticker(ticker, use_real_api=True) results.append(r) except Exception as e: print(f"\n=== {ticker} ===\n КРИТИЧЕСКАЯ ОШИБКА: {e}") results.append({'ticker': ticker, 'class': classify_ticker(ticker), 'error': str(e), 'signal': 'HOLD', 'confidence': 0}) # ── Сводка ── print(f"\n{'=' * 72}") print("СВОДКА РЕЗУЛЬТАТОВ") print(f"{'=' * 72}") signals = {'BUY': [], 'SELL': [], 'HOLD': []} for r in results: sig = r.get('signal', 'HOLD') if sig not in signals: signals[sig] = [] signals[sig].append(r) print(f"\nBUY: {len(signals['BUY'])} | SELL: {len(signals['SELL'])} | HOLD: {len(signals['HOLD'])}") 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"\n--- {sig_type} Signals ---") print(f"{'Тикер':8s} {'Conf':>5s} {'Цена':>10s} {'W1':>10s} {'D1':>10s} {'H1':>10s} {'Wyckoff':30s} {'OB':18s} {'RR':>6s}") for r in tickers_sig: conf = r.get('confidence', 0) price = r.get('tbank_price') or r.get('entry') or 0 ob_v = r.get('orderbook', {}).get('verdict', '—') if r.get('orderbook') else 'НЕТ' wyck = (r.get('wyckoff_phase', '—') or '—')[:28] rr = r.get('rr') rr_str = f"1:{rr}" if rr else '—' print(f"{r['ticker']:8s} {conf:4.0f}% {price:>10.2f} " f"{r.get('trend_w1','—'):>10s} {r.get('trend_d1','—'):>10s} " f"{r.get('trend_h1','—'):>10s} {wyck:30s} {ob_v:18s} {rr_str:>6s}") # HOLD if signals['HOLD']: print(f"\n--- HOLD ({len(signals['HOLD'])}) ---") for r in signals['HOLD']: price = r.get('tbank_price') or r.get('entry') or 0 err = r.get('error', '') err_str = f" [err: {err}]" if err else '' print(f" {r['ticker']:8s} ({r.get('class','?'):6s}) price={price:>10.2f} " f"W1:{r.get('trend_w1','—')} D1:{r.get('trend_d1','—')} H1:{r.get('trend_h1','—')}{err_str}") # Сохранение результатов output = { 'timestamp': datetime.now().isoformat(), 'env': 'real', 'total_tickers': len(results), 'summary': { 'BUY': len(signals['BUY']), 'SELL': len(signals['SELL']), 'HOLD': len(signals['HOLD']), }, 'results': results, } out_path = '/tmp/moex_full_analysis_results.json' with open(out_path, 'w', encoding='utf-8') as f: json.dump(output, f, indent=2, ensure_ascii=False, default=str) print(f"\nРезультаты сохранены в {out_path}") return results if __name__ == '__main__': main()