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薇薇安
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@ -390,6 +390,12 @@ async def get_global_configs(
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"category": "scan",
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"description": "严格成交量过滤，24H Volume低于此值（USD）直接剔除",
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},
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"EXCLUDE_MAJOR_COINS": {
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"value": True,
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"type": "boolean",
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"category": "scan",
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"description": "是否排除主流币（BTC、ETH、BNB等），专注于山寨币。山寨币策略建议开启。",
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},
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"USE_TRAILING_STOP": {
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"value": False,
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"type": "boolean",
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@ -745,7 +745,6 @@ class ConfigManager:
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# 涨跌幅阈值
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'MIN_CHANGE_PERCENT': eff_get('MIN_CHANGE_PERCENT', 2.0),
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'TOP_N_SYMBOLS': eff_get('TOP_N_SYMBOLS', 10),
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# 风险控制
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'STOP_LOSS_PERCENT': eff_get('STOP_LOSS_PERCENT', 0.10), # 默认10%
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@ -767,8 +766,9 @@ class ConfigManager:
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# 市场扫描（1小时主周期）
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'SCAN_INTERVAL': eff_get('SCAN_INTERVAL', 3600), # 1小时
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'TOP_N_SYMBOLS': eff_get('TOP_N_SYMBOLS', 10), # 每次扫描后处理的交易对数量
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'MAX_SCAN_SYMBOLS': eff_get('MAX_SCAN_SYMBOLS', 500), # 扫描的最大交易对数量（0表示扫描所有）
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'TOP_N_SYMBOLS': eff_get('TOP_N_SYMBOLS', 8), # 每次扫描后处理的交易对数量（增加到8，给更多选择余地）
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'MAX_SCAN_SYMBOLS': eff_get('MAX_SCAN_SYMBOLS', 250), # 扫描的最大交易对数量（增加到250，提升覆盖率到46%）
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'EXCLUDE_MAJOR_COINS': eff_get('EXCLUDE_MAJOR_COINS', True), # 是否排除主流币（BTC、ETH、BNB等），专注于山寨币
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'KLINE_INTERVAL': eff_get('KLINE_INTERVAL', '1h'),
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'PRIMARY_INTERVAL': eff_get('PRIMARY_INTERVAL', '1h'),
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'CONFIRM_INTERVAL': eff_get('CONFIRM_INTERVAL', '4h'),
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@ -82,9 +82,9 @@ const ConfigPanel = () => {
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USE_FIXED_RISK_SIZING: true, FIXED_RISK_PERCENT: 1.0,
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USE_TRAILING_STOP: true, TRAILING_STOP_ACTIVATION: 30.0, TRAILING_STOP_PROTECT: 15.0,
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MAX_POSITION_PERCENT: 1.5, MAX_TOTAL_POSITION_PERCENT: 12.0, MAX_DAILY_ENTRIES: 5,
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MIN_VOLUME_24H: 30000000, MIN_VOLATILITY: 3.0, TOP_N_SYMBOLS: 5, MIN_SIGNAL_STRENGTH: 5,
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SCAN_INTERVAL: 3600, SMART_ENTRY_ENABLED: true, AUTO_TRADE_ONLY_TRENDING: true,
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AUTO_TRADE_ALLOW_4H_NEUTRAL: true,
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MIN_VOLUME_24H: 30000000, MIN_VOLATILITY: 3.0, TOP_N_SYMBOLS: 8, MIN_SIGNAL_STRENGTH: 5,
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MAX_SCAN_SYMBOLS: 250, SCAN_INTERVAL: 3600, SMART_ENTRY_ENABLED: true, AUTO_TRADE_ONLY_TRENDING: true,
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AUTO_TRADE_ALLOW_4H_NEUTRAL: true, EXCLUDE_MAJOR_COINS: true,
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},
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},
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swing: {
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@ -358,9 +358,10 @@ const GlobalConfig = () => {
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MIN_VOLUME_24H: 30000000, // 24H成交额≥3000万美元
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MIN_VOLUME_24H_STRICT: 50000000, // 严格过滤≥5000万
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MIN_VOLATILITY: 3.0, // 最小波动率3%
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TOP_N_SYMBOLS: 5, // 只做最强5个
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MAX_SCAN_SYMBOLS: 150, // 扫描前150个
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TOP_N_SYMBOLS: 8, // 选择信号最强的8个（给更多选择余地，避免错过好机会）
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MAX_SCAN_SYMBOLS: 250, // 扫描前250个（增加覆盖率，从27.6%提升到46.0%）
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MIN_SIGNAL_STRENGTH: 5, // 信号强度≥5（MACD金叉/死叉已足够，配合其他筛选）
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EXCLUDE_MAJOR_COINS: true, // 排除主流币（BTC、ETH、BNB等），专注于山寨币
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// 时间框架
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PRIMARY_INTERVAL: '4h', // 主周期4小时
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@ -565,9 +565,9 @@ class BinanceClient:
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'ts': now_ms
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}
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# 写入 Redis 缓存（TTL: 30秒）
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await self.redis_cache.set(cache_key, result, ttl=30)
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logger.debug(f"批量获取到 {len(result)} 个交易对的24小时行情数据，已缓存")
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# 写入 Redis 缓存（TTL: 60秒，多个账户可以共用）
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await self.redis_cache.set(cache_key, result, ttl=60)
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logger.debug(f"批量获取到 {len(result)} 个交易对的24小时行情数据，已缓存 (TTL: 60秒)")
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return result
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except BinanceAPIException as e:
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error_code = e.code if hasattr(e, 'code') else None
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@ -213,8 +213,9 @@ def _get_trading_config():
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'MIN_POSITION_PERCENT': 0.01, # 最小仓位1%
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'MIN_MARGIN_USDT': 5.0, # 最小保证金5美元
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'MIN_CHANGE_PERCENT': 0.5, # 最小价格变动0.5%
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'TOP_N_SYMBOLS': 5, # 只做信号最强的5个，专注优质机会
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'MAX_SCAN_SYMBOLS': 150, # 扫描前150个，覆盖主流山寨
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'TOP_N_SYMBOLS': 8, # 选择信号最强的8个（给更多选择余地，避免错过好机会）
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'MAX_SCAN_SYMBOLS': 250, # 扫描前250个（增加覆盖率，从27.6%提升到46.0%）
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'EXCLUDE_MAJOR_COINS': True, # 排除主流币（BTC、ETH、BNB等），专注于山寨币
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'STOP_LOSS_PERCENT': 0.15, # 止损15%（山寨币波动大，止损要宽）
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'TAKE_PROFIT_PERCENT': 0.60, # 止盈60%（4:1盈亏比，追求大赢家）
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'MIN_STOP_LOSS_PRICE_PCT': 0.02, # 最小止损价格变动2%
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@ -71,6 +71,22 @@ class MarketScanner:
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symbols = all_symbols
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logger.info(f"扫描所有 {len(symbols)} 个USDT交易对")
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# 过滤主流币（山寨币策略应该排除主流币）
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exclude_major_coins = cfg.get('EXCLUDE_MAJOR_COINS', True)
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if exclude_major_coins:
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# 主流币列表（市值排名前15的主流币）
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major_coins = {
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'BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'SOLUSDT', 'XRPUSDT',
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'ADAUSDT', 'DOGEUSDT', 'DOTUSDT', 'AVAXUSDT', 'MATICUSDT',
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'LINKUSDT', 'UNIUSDT', 'ATOMUSDT', 'ETCUSDT', 'LTCUSDT',
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'NEARUSDT', 'APTUSDT', 'ARBUSDT', 'OPUSDT', 'SUIUSDT'
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}
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symbols_before = len(symbols)
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symbols = [s for s in symbols if s not in major_coins]
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excluded_count = symbols_before - len(symbols)
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if excluded_count > 0:
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logger.info(f"排除主流币 {excluded_count} 个，剩余 {len(symbols)} 个交易对（专注于山寨币）")
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# 先批量获取所有交易对的24小时行情数据（减少API请求）
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logger.info(f"批量获取 {len(symbols)} 个交易对的24小时行情数据...")
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all_tickers = await self.client.get_all_tickers_24h()
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@ -247,20 +263,66 @@ class MarketScanner:
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change_percent = ((current_price - prev_price) / prev_price) * 100
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# 计算技术指标
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rsi = TechnicalIndicators.calculate_rsi(close_prices, period=14)
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macd = TechnicalIndicators.calculate_macd(close_prices)
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bollinger = TechnicalIndicators.calculate_bollinger_bands(close_prices, period=20)
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atr_period = int(config.TRADING_CONFIG.get('ATR_PERIOD', 14) or 14)
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atr = TechnicalIndicators.calculate_atr(high_prices, low_prices, close_prices, period=atr_period)
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ema20 = TechnicalIndicators.calculate_ema(close_prices, period=20)
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ema50 = TechnicalIndicators.calculate_ema(close_prices, period=50)
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# ⚠️ 优化：检查技术指标计算结果缓存（基于K线数据的最后更新时间）
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# 如果K线数据没有更新，可以直接使用缓存的技术指标
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primary_interval = config.TRADING_CONFIG.get('PRIMARY_INTERVAL', '1h')
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confirm_interval = config.TRADING_CONFIG.get('CONFIRM_INTERVAL', '4h')
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cache_key_indicators = f"indicators:{symbol}:{primary_interval}:{confirm_interval}"
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last_kline_time = int(klines[-1][0]) if klines else 0 # 最后一根K线的时间戳
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# 计算4H周期的EMA20用于多周期共振检查
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ema20_4h = TechnicalIndicators.calculate_ema(close_prices_4h, period=20) if len(close_prices_4h) >= 20 else None
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# 尝试从缓存获取技术指标计算结果
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cached_indicators = None
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try:
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cached_indicators = await self.client.redis_cache.get(cache_key_indicators)
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except Exception:
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pass
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# 判断市场状态
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market_regime = TechnicalIndicators.detect_market_regime(close_prices)
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use_cached_indicators = False
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if cached_indicators and cached_indicators.get('last_kline_time') == last_kline_time:
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# 缓存命中，使用缓存的技术指标
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use_cached_indicators = True
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logger.debug(f"{symbol} 使用缓存的技术指标计算结果")
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rsi = cached_indicators.get('rsi')
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macd = cached_indicators.get('macd')
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bollinger = cached_indicators.get('bollinger')
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atr = cached_indicators.get('atr')
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ema20 = cached_indicators.get('ema20')
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ema50 = cached_indicators.get('ema50')
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ema20_4h = cached_indicators.get('ema20_4h')
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market_regime = cached_indicators.get('marketRegime')
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else:
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# 缓存未命中，重新计算技术指标
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rsi = TechnicalIndicators.calculate_rsi(close_prices, period=14)
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macd = TechnicalIndicators.calculate_macd(close_prices)
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bollinger = TechnicalIndicators.calculate_bollinger_bands(close_prices, period=20)
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atr_period = int(config.TRADING_CONFIG.get('ATR_PERIOD', 14) or 14)
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atr = TechnicalIndicators.calculate_atr(high_prices, low_prices, close_prices, period=atr_period)
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ema20 = TechnicalIndicators.calculate_ema(close_prices, period=20)
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ema50 = TechnicalIndicators.calculate_ema(close_prices, period=50)
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# 计算4H周期的EMA20用于多周期共振检查
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ema20_4h = TechnicalIndicators.calculate_ema(close_prices_4h, period=20) if len(close_prices_4h) >= 20 else None
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# 判断市场状态
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market_regime = TechnicalIndicators.detect_market_regime(close_prices)
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# 保存技术指标计算结果到缓存（TTL: 30秒，与K线缓存一致）
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try:
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indicators_cache = {
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'last_kline_time': last_kline_time,
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'rsi': rsi,
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'macd': macd,
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'bollinger': bollinger,
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'atr': atr,
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'ema20': ema20,
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'ema50': ema50,
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'ema20_4h': ema20_4h,
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'marketRegime': market_regime,
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}
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await self.client.redis_cache.set(cache_key_indicators, indicators_cache, ttl=30)
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logger.debug(f"{symbol} 技术指标计算结果已缓存 (TTL: 30秒)")
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except Exception as e:
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logger.debug(f"{symbol} 缓存技术指标计算结果失败: {e}")
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# 计算交易信号得分（用于排序）
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signal_score = 0
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