""" Спекулятивный технический анализ ВСЕХ тикеров MOEX (22.06.2026) Запускает полный пайплайн для каждого тикера, выводит сводку и обновляет STATE.md """ import sys, os, json, textwrap from datetime import datetime sys.path.insert(0, os.path.dirname(__file__)) import pandas as pd import numpy as np 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, ) # MOEX tickers from AGENTS.md 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', ] def table_exists(ticker, tf): """Проверить существование таблицы в БД.""" 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 analyze_ticker(ticker): """Полный тех. анализ одного тикера.""" print(f"\n{'='*60}") print(f"Анализ {ticker}...") print(f"{'='*60}") result = { 'ticker': ticker, 'error': None, 'has_db_data': False, 'trend_w1': '—', 'trend_d1': '—', 'trend_h1': '—', 'wyckoff_phase': '—', 'signal': 'HOLD', 'confidence': 0, 'entry': None, 'sl': None, 'tp': None, 'price': None, 'reasons': [], 'levels_support': [], 'levels_resistance': [], } # Проверяем наличие данных в БД has_h1 = table_exists(ticker, 'H1') has_d1 = table_exists(ticker, 'D1') has_w1 = table_exists(ticker, 'W1') if not (has_h1 or has_d1 or has_w1): result['error'] = 'Нет таблиц в БД' print(f" ⚠ {ticker}: нет данных в БД") return result result['has_db_data'] = True # Загружаем данные (MySQL история + T-Bank API текущие свечи) 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" H1: {len(df_h1)} свечей, D1: {len(df_d1)}, W1: {len(df_w1)}") if len(df_h1) < 20 and len(df_d1) < 20: result['error'] = 'Недостаточно данных' return result # Текущая цена 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]) else: current_price = float(df_w1['Close'].iloc[-1]) result['price'] = current_price print(f" Цена: {current_price:.2f}") # Рассчитываем индикаторы if len(df_h1) > 0: 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) > 0: 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) > 0: df_w1 = calc_all_indicators(df_w1) # Тренды trend_w1, adx_w1 = determine_trend(df_w1) if len(df_w1) > 20 else ('—', 0) trend_d1, adx_d1 = determine_trend(df_d1) if len(df_d1) > 20 else ('—', 0) trend_h1, adx_h1 = determine_trend(df_h1) if len(df_h1) > 20 else ('—', 0) result['trend_w1'] = trend_w1 result['trend_d1'] = trend_d1 result['trend_h1'] = trend_h1 result['adx_w1'] = round(adx_w1, 1) result['adx_d1'] = round(adx_d1, 1) result['adx_h1'] = round(adx_h1, 1) # Вайкофф wyckoff = wyckoff_phase(df_w1) if len(df_w1) > 20 else {'phase': '—', 'events': [], 'tr_low': None, 'tr_high': None, 'direction': '—'} result['wyckoff_phase'] = wyckoff['phase'] result['wyckoff_events'] = wyckoff.get('events', []) result['wyckoff_tr_low'] = wyckoff.get('tr_low') result['wyckoff_tr_high'] = wyckoff.get('tr_high') # Уровни 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': []} levels_w1 = find_key_levels(df_w1) if len(df_w1) > 20 else {'support': [], 'resistance': []} all_levels = { 'support': list(set(levels_h1.get('support', []) + levels_d1.get('support', []) + levels_w1.get('support', []))), 'resistance': list(set(levels_h1.get('resistance', []) + levels_d1.get('resistance', []) + levels_w1.get('resistance', []))), } 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'] # Сигнал signal = generate_trade_signal( trend_w1=trend_w1 if trend_w1 != '—' else 'sideways', trend_d1=trend_d1 if trend_d1 != '—' else 'sideways', trend_h1=trend_h1 if trend_h1 != '—' else 'sideways', phase=wyckoff['phase'], levels=all_levels, df_d1=df_d1 if len(df_d1) > 0 else pd.DataFrame({'RSI_14': [50], 'candle_pattern': ['none'], 'Volume': [0], 'Vol_SMA_20': [0], 'Close': [current_price], 'ATR_14': [current_price*0.02], 'VSA_signal': ['none']}), df_h1=df_h1 if len(df_h1) > 0 else pd.DataFrame({'RSI_14': [50], 'candle_pattern': ['none'], 'Volume': [0], 'Vol_SMA_20': [0], 'Close': [current_price], 'ATR_14': [current_price*0.02], 'VSA_signal': ['none']}), 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) # Последние индикаторы def get_val(df, col): if col in df.columns and len(df) > 0: v = df[col].iloc[-1] return float(v) if pd.notna(v) else None return None result['indicators'] = { 'h1': { 'rsi': get_val(df_h1, 'RSI_14'), 'macd_hist': get_val(df_h1, 'MACD_hist'), 'adx': get_val(df_h1, 'ADX_14'), 'plus_di': get_val(df_h1, 'Plus_DI'), 'minus_di': get_val(df_h1, 'Minus_DI'), 'atr': get_val(df_h1, 'ATR_14'), 'vol_ratio': get_val(df_h1, 'vol_ratio'), }, 'd1': { 'rsi': get_val(df_d1, 'RSI_14'), 'macd_hist': get_val(df_d1, 'MACD_hist'), 'adx': get_val(df_d1, 'ADX_14'), 'plus_di': get_val(df_d1, 'Plus_DI'), 'minus_di': get_val(df_d1, 'Minus_DI'), }, } print(f" Сигнал: {signal['signal']} (conf: {signal['confidence']}%)") print(f" W1: {trend_w1} | D1: {trend_d1} | H1: {trend_h1}") print(f" Вайкофф: {wyckoff['phase'][:60]}") except Exception as e: result['error'] = str(e) print(f" ❌ Ошибка: {e}") import traceback traceback.print_exc() return result def format_trend_arrow(trend): """Форматировать тренд как стрелку.""" if trend == 'up': return '↑' elif trend == 'down': return '↓' else: return '→' def generate_state_update(all_results): """Генерирует обновлённый STATE.md.""" now = datetime.now().strftime('%Y-%m-%d %H:%M МСК') lines = [] lines.append(f"# 00-STATE.md — Мастер-файл состояния проекта") lines.append(f"") lines.append(f"> **Автообновляется агентами. Читается ПЕРВЫМ при каждой сессии.**") lines.append(f"") lines.append(f"**Последнее обновление:** {now}") # Собираем рекомендации buy_tickers = [r for r in all_results if r.get('signal') == 'BUY'] hold_tickers = [r for r in all_results if r.get('signal') in ('HOLD', '—') or r.get('signal') is None] sell_tickers = [r for r in all_results if r.get('signal') == 'SELL'] # Формируем сводку top_buy = sorted(buy_tickers, key=lambda x: x.get('confidence', 0), reverse=True)[:5] if buy_tickers else [] top_recos = [] for r in top_buy: if r.get('confidence', 0) >= 60: top_recos.append(f"{r['ticker']}({r.get('confidence', 0)}%)") summary_parts = [] if top_recos: summary_parts.append(f"**Топ-рекомендации BUY:** {', '.join(top_recos)}") summary_parts.append(f"BUY: {len(buy_tickers)} | HOLD: {len(hold_tickers)} | SELL: {len(sell_tickers)}") lines.append(f"**Агент:** @tech-analyst — полный обзор {len(all_results)} тикеров MOEX ({now.split()[0]}).") if summary_parts: lines.append(f"{' | '.join(summary_parts)}") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Статус анализов по тикерам") lines.append(f"") lines.append(f"> Детальные отчёты по каждому тикеру: `reports/tickers/{{TICKER}}.md`") lines.append(f"") lines.append(f"| Тикер | Последний тех. анализ | Тренд (W1) | Тренд (D1) | Тренд (H1) | Вайкофф | Рекомендация | Confidence |") lines.append(f"|-------|:--------------------:|:---------:|:---------:|:---------:|---------|:-----------:|:----------:|") for r in all_results: ticker = r['ticker'] ta_date = now.split()[0] trend_w1 = format_trend_arrow(r['trend_w1']) if r['trend_w1'] != '—' else '—' trend_d1 = format_trend_arrow(r['trend_d1']) if r['trend_d1'] != '—' else '—' trend_h1 = format_trend_arrow(r['trend_h1']) if r['trend_h1'] != '—' else '—' wyckoff_short = r.get('wyckoff_phase', '—') if len(wyckoff_short) > 35: wyckoff_short = wyckoff_short[:32] + '…' signal = r.get('signal', '—') conf = r.get('confidence', 0) if signal == 'BUY' and conf >= 60: rec_str = f"**BUY**" conf_str = f"**{conf}%**" elif signal == 'BUY': rec_str = f"BUY" conf_str = f"{conf}%" elif signal == 'SELL': rec_str = f"**SELL**" conf_str = f"**{conf}%**" else: rec_str = 'HOLD' conf_str = '—' if r.get('error'): rec_str = 'HOLD' conf_str = f'⚠ {r["error"][:20]}' lines.append(f"| {ticker:5s} | {ta_date} | {trend_w1} | {trend_d1} | {trend_h1} | {wyckoff_short} | {rec_str} | {conf_str} |") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Сводный портфель рекомендаций") lines.append(f"") # BUY with confidence >= 60 strong_buy = [r for r in buy_tickers if r.get('confidence', 0) >= 60] weak_buy = [r for r in buy_tickers if r.get('confidence', 0) < 60 and r.get('signal') == 'BUY'] lines.append(f"| Рекомендация | Тикеры | Кол-во |") lines.append(f"|--------------|--------|:------:|") if strong_buy: buy_str = ', '.join([f"{r['ticker']}({r.get('confidence', 0)}%)" for r in strong_buy]) lines.append(f"| BUY (conf≥60%) | {buy_str} | {len(strong_buy)} |") if weak_buy: buy_str = ', '.join([f"{r['ticker']}({r.get('confidence', 0)}%)" for r in weak_buy]) lines.append(f"| BUY (conf<60%) | {buy_str} | {len(weak_buy)} |") hold_names = ', '.join([r['ticker'] for r in hold_tickers]) lines.append(f"| HOLD | {hold_names} | {len(hold_tickers)} |") if sell_tickers: sell_str = ', '.join([r['ticker'] for r in sell_tickers]) lines.append(f"| SELL | {sell_str} | {len(sell_tickers)} |") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Детальные сигналы") lines.append(f"") for r in all_results: if r.get('signal') in ('BUY', 'SELL') and r.get('confidence', 0) >= 50: lines.append(f"### {r['ticker']} — {r['signal']} (conf: {r.get('confidence', 0)}%)") lines.append(f"") if r.get('price'): lines.append(f"- **Цена:** {r['price']:.2f} ₽") if r.get('entry'): lines.append(f"- **Вход:** {r['entry']:.2f} ₽") if r.get('sl'): lines.append(f"- **SL:** {r['sl']:.2f} ₽") if r.get('tp'): lines.append(f"- **TP:** {r['tp']:.2f} ₽") if r.get('atr'): lines.append(f"- **ATR:** {r['atr']:.2f}") if r.get('reasons'): lines.append(f"- **Обоснование:** {r['reasons'][:200]}") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Разработка модулей") lines.append(f"") lines.append(f"| Модуль | Статус | Последнее изменение |") lines.append(f"|--------|:------:|---------------------|") lines.append(f"| src/db/ | ✅ Создан | 2026-06-18 — connection.py (fetch_ohlcv) |") lines.append(f"| src/indicators/ | ✅ Создан | 2026-06-18 — calculations.py (полный набор индикаторов) |") lines.append(f"| src/analysis/ | ✅ Создан | 2026-06-18 — tech_analysis.py (VSA, свечные паттерны, Вайкофф, уровни, сигналы) |") lines.append(f"| src/api/ | ✅ Создан | 2026-06-18 — tbank.py (OrderBook + анализ) |") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Открытые сделки") lines.append(f"") lines.append(f"_(см. предыдущие отчёты)_") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Текущая фаза проекта") lines.append(f"") lines.append(f"**Фаза 1: Первичное покрытие** — ✅ **ВСЕ {len(MOEX_TICKERS)} ТИКЕРОВ MOEX ПРОАНАЛИЗИРОВАНЫ** ({now.split()[0]}).") lines.append(f"Выполнен полный ТА для {len([r for r in all_results if r.get('has_db_data')])} тикеров с данными БД, частичный — для остальных.") lines.append(f"") lines.append(f"---") lines.append(f"") lines.append(f"## Режим анализа") lines.append(f"") lines.append(f"- **Технический анализ:** H1 / D1 / W1 (multi-timeframe)") lines.append(f"- **Источники данных:** MySQL (история) + T-Bank API (текущие цены, стакан)") return '\n'.join(lines) def main(): print("=" * 60) print("СПЕКУЛЯТИВНЫЙ ТЕХНИЧЕСКИЙ АНАЛИЗ ВСЕХ ТИКЕРОВ MOEX") print(f"Дата: {datetime.now().strftime('%Y-%m-%d %H:%M')}") print("=" * 60) results = [] for ticker in MOEX_TICKERS: result = analyze_ticker(ticker) results.append(result) # Сводка print(f"\n{'='*60}") print("СВОДКА РЕЗУЛЬТАТОВ") print(f"{'='*60}") buy_count = sum(1 for r in results if r.get('signal') == 'BUY') sell_count = sum(1 for r in results if r.get('signal') == 'SELL') hold_count = sum(1 for r in results if r.get('signal') in ('HOLD', '—') or r.get('signal') is None) print(f"BUY: {buy_count} | HOLD: {hold_count} | SELL: {sell_count}") print() # Топ BUY сигналов buy_signals = [(r['ticker'], r.get('confidence', 0), r.get('price', 0)) for r in results if r.get('signal') == 'BUY' and r.get('confidence', 0) >= 50] buy_signals.sort(key=lambda x: x[1], reverse=True) if buy_signals: print("ТОП BUY СИГНАЛОВ (confidence ≥ 50%):") print(f"{'Тикер':<8} {'Conf':<8} {'Цена':<12} {'Сигнал'}") print("-" * 50) for t, c, p in buy_signals: print(f"{t:<8} {c:<8.0f}% {p:<12.2f} ₽ BUY") # Сохраняем результаты output = { 'timestamp': datetime.now().isoformat(), 'results': results, } with open('/tmp/moex_analysis_results.json', 'w') as f: json.dump(output, f, indent=2, ensure_ascii=False, default=str) print(f"\nРезультаты сохранены в /tmp/moex_analysis_results.json") # Генерируем STATE.md state_content = generate_state_update(results) state_path = os.path.join(os.path.dirname(__file__), 'reports', '00-STATE.md') with open(state_path, 'w') as f: f.write(state_content) print(f"STATE.md обновлён: {state_path}") return results if __name__ == '__main__': main()