Add A share to display
This commit is contained in:
Porter
+232
-50
@@ -13,11 +13,13 @@ import time
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import traceback
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from pytz import timezone
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import talib.abstract as ta
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import numpy as np
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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ChanLun import ChanLun
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from ChanEnum import Chan_BI_DIR, Chan_SEG_DIR, Chan_KLC_FX, Chan_FX_TYPE
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from cn_stock_data import ChinaStockData
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# 添加买卖点枚举类型
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class TRADE_POINT_TYPE:
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@@ -35,6 +37,9 @@ exchange = ccxt.binance({
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'enableRateLimit': True,
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})
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# 初始化A股数据获取器
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china_stock = ChinaStockData()
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# 时间周期映射
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TIMEFRAMES = {
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'1m': '1分钟',
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@@ -54,8 +59,32 @@ SYMBOLS = [
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'ADA/USDT:USDT', 'DOGE/USDT:USDT', 'AVAX/USDT:USDT', 'DOT/USDT:USDT', 'MATIC/USDT:USDT'
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]
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# A股热门股票
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A_STOCK_SYMBOLS = china_stock.get_popular_stocks()
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def detect_symbol_type(symbol):
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"""检测交易对类型：crypto 或 a_stock"""
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if '/' in symbol and 'USDT' in symbol:
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return 'crypto'
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elif len(symbol) == 6 and symbol.isdigit():
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return 'a_stock'
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else:
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return 'unknown'
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def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""获取K线数据，支持分页加载确保获取指定时间范围内的所有数据"""
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"""获取K线数据，支持加密货币和A股"""
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symbol_type = detect_symbol_type(symbol)
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if symbol_type == 'crypto':
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return get_crypto_kl_data(symbol, timeframe, limit, start_time, end_time)
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elif symbol_type == 'a_stock':
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return get_a_stock_kl_data(symbol, timeframe, limit, start_time, end_time)
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else:
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print(f"未知的交易对类型: {symbol}")
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return None
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def get_crypto_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""获取加密货币K线数据，支持分页加载确保获取指定时间范围内的所有数据"""
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try:
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# 初始化参数
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since = None
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@@ -73,42 +102,73 @@ def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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except ValueError:
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print(f"无效的结束时间: {end_time}")
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# 根据时间周期调整每次请求的数据量
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batch_size = 1000 # 默认批次大小
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if timeframe in ['1m', '3m', '5m']:
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batch_size = 500 # 分钟级数据减少批次大小
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elif timeframe in ['15m', '30m', '1h']:
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batch_size = 1000
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else:
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batch_size = 1500 # 日线及以上可以获取更多
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# 初始化存储所有K线数据的列表
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all_ohlcv = []
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# 初始化当前查询的开始时间
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current_since = since
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# 添加请求计数和最大限制
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request_count = 0
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max_requests = 50 # 最大请求次数，防止无限循环
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print(f"开始分批获取加密货币数据: {symbol}, {timeframe}")
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# 分页加载数据
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while True:
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print(f"获取数据: {symbol}, {timeframe}, limit={limit}, since={current_since}")
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while request_count < max_requests:
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request_count += 1
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# 获取当前页的数据
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ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=limit)
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print(f"批次 {request_count}: 获取数据 since={current_since}, limit={batch_size}")
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# 如果没有获取到数据，结束循环
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if not ohlcv or len(ohlcv) == 0:
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break
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try:
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# 获取当前页的数据
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ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=batch_size)
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# 将获取到的数据添加到总列表中
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all_ohlcv.extend(ohlcv)
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# 获取最后一条数据的时间戳
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last_timestamp = ohlcv[-1][0]
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# 如果已达到结束时间，结束循环
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if until and last_timestamp >= until:
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break
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# 如果没有获取到数据，结束循环
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if not ohlcv or len(ohlcv) == 0:
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print(f"批次 {request_count}: 未获取到数据，结束")
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break
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# 将获取到的数据添加到总列表中
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all_ohlcv.extend(ohlcv)
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print(f"批次 {request_count}: 获取到 {len(ohlcv)} 条记录")
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# 如果获取的数据条数小于限制数，说明已经获取完所有数据
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if len(ohlcv) < limit:
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break
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# 获取最后一条数据的时间戳
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last_timestamp = ohlcv[-1][0]
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# 更新下一页的开始时间（加1毫秒避免重复）
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current_since = last_timestamp + 1
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# 如果已达到结束时间，结束循环
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if until and last_timestamp >= until:
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print(f"批次 {request_count}: 已达到结束时间，结束")
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break
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# 如果获取的数据条数小于限制数，说明已经获取完所有数据
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if len(ohlcv) < batch_size:
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print(f"批次 {request_count}: 数据不足批次大小，已获取完所有数据")
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break
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# 更新下一页的开始时间（加1毫秒避免重复）
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current_since = last_timestamp + 1
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except Exception as e:
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print(f"批次 {request_count} 获取失败: {e}")
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# 如果单个批次失败，继续尝试下一个批次
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if current_since:
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# 尝试增加时间跳过可能的问题时间点
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current_since += 60000 # 跳过1分钟
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else:
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break
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# 防止API请求过于频繁
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time.sleep(0.5) # 等待0.5秒
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time.sleep(0.3) # 减少到0.3秒提高效率
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# 数据为空的情况
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if not all_ohlcv or len(all_ohlcv) == 0:
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@@ -128,33 +188,76 @@ def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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# 按时间排序
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df = df.sort_values('timestamp')
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"""
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chan = ChanLun()
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klc_list = chan.get_klc_list(df)
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klc_index = 0
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df_copy = df.copy()
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ret_df = pd.DataFrame(columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'date'])
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for index in range(0, len(df)):
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if klc_index >= len(klc_list):
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klc_index = len(klc_list) - 1
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klc = klc_list[klc_index]
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if index == klc.start_klu.index:
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ret_df.loc[klc_index] = df_copy.loc[index]
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klc_index += 1
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"""
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# 限制数据条数的逻辑 - 优先考虑时间范围
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if start_time and end_time:
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# 如果指定了明确的时间范围，返回该时间范围内的所有数据
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print(f"用户指定了时间范围，返回完整数据 {len(df)} 条记录")
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if len(df) > 10000: # 防止数据量过大，设置一个合理的上限
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print(f"警告：数据量过大({len(df)}条)，为保证性能将限制为最新的10000条记录")
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df = df.tail(10000).reset_index(drop=True)
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elif limit and len(df) > limit:
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# 如果没有指定明确时间范围，使用默认的limit限制
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print(f"未指定明确时间范围，应用默认限制，返回最新的 {limit} 条记录")
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df = df.tail(limit).reset_index(drop=True)
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df = add_indicators(df)
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# 如果过滤后没有数据，返回None
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if len(df) == 0:
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print("过滤后无数据")
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return None
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print(f"获取到总共 {len(df)} 条数据")
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print(f"成功获取加密货币数据: {len(df)} 条记录 (共 {request_count} 个批次)")
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return df
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except Exception as e:
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print(f"获取数据错误: {e}")
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print(f"获取加密货币数据错误: {e}")
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traceback.print_exc()
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return None
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def get_a_stock_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""获取A股K线数据"""
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try:
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# 处理时间戳参数转换为日期字符串
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start_date = None
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end_date = None
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if start_time:
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try:
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start_timestamp = int(start_time)
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start_date = datetime.fromtimestamp(start_timestamp / 1000).strftime('%Y-%m-%d')
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except:
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start_date = start_time
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if end_time:
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try:
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end_timestamp = int(end_time)
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end_date = datetime.fromtimestamp(end_timestamp / 1000).strftime('%Y-%m-%d')
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except:
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end_date = end_time
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# 如果用户指定了时间范围，优先获取该范围内的所有数据
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actual_limit = limit
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if start_date and end_date:
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print(f"用户指定了时间范围 {start_date} 到 {end_date}，将获取该范围内的所有数据")
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actual_limit = None # 不限制数据条数，获取完整时间范围数据
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# 调用A股数据获取器
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df = china_stock.get_kl_data(symbol, timeframe, start_date, end_date, actual_limit)
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if df is None:
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print(f"未获取到A股数据: {symbol}")
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return None
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print(f"获取到A股数据: {len(df)} 条记录")
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return df
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except Exception as e:
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print(f"获取A股数据错误: {e}")
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traceback.print_exc()
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return None
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def add_indicators(df):
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fast = 8
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slow = 16
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@@ -178,12 +281,17 @@ def add_indicators(df):
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df['ma250'] = df['ma250'].fillna(0)
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df['rsi'] = df['rsi'].fillna(0)
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df['avg_volume'] = df['volume'].rolling(10).mean()
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# 计算量比
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# 计算量比，避免产生Infinity值
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df['volume_ratio'] = df['volume'] / df['avg_volume']
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# 填充缺失值（前N根K线）
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df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
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df['avg_volume'] = df['avg_volume'].fillna(0)
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# 处理Infinity和-Infinity值
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df['volume_ratio'] = df['volume_ratio'].replace([float('inf'), float('-inf')], 1.0)
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return df
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def calculate_macd(df):
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"""计算MACD指标"""
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exp1 = df['close'].ewm(span=12, adjust=False).mean()
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@@ -287,14 +395,10 @@ def identify_trade_points(bi_list, seg_list, zs_list):
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# 辅助函数，转换缠论方向枚举为整数
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def convert_direction(direction):
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"""将缠论方向枚举转换为整数"""
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if direction == Chan_BI_DIR.UP:
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"""转换方向枚举为数字"""
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if direction == Chan_BI_DIR.UP or direction == Chan_SEG_DIR.UP:
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return 1
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elif direction == Chan_BI_DIR.DOWN:
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return -1
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elif direction == Chan_SEG_DIR.UP:
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return 1
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elif direction == Chan_SEG_DIR.DOWN:
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elif direction == Chan_BI_DIR.DOWN or direction == Chan_SEG_DIR.DOWN:
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return -1
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else:
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return 0
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@@ -380,10 +484,34 @@ def is_smaller_or_equal_timeframe(tf1, tf2):
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# 返回tf1是否小于等于tf2
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return tf1_value <= tf2_value
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def clean_dataframe_for_json(df):
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"""清理DataFrame中的NaN值，确保JSON序列化正常"""
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# 创建副本以避免修改原数据
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df_clean = df.copy()
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# 将NaN、inf、-inf替换为None
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df_clean = df_clean.replace([np.nan, np.inf, -np.inf], None)
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# 处理数值列，确保值为有限数字或None
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numeric_columns = df_clean.select_dtypes(include=[np.number]).columns
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for col in numeric_columns:
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# 确保所有数值都是有限的
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df_clean[col] = df_clean[col].apply(lambda x: x if (x is not None and np.isfinite(x)) else None)
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# 处理时间列，确保格式正确
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datetime_columns = df_clean.select_dtypes(include=['datetime64']).columns
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for col in datetime_columns:
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df_clean[col] = df_clean[col].dt.strftime('%Y-%m-%d %H:%M:%S')
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return df_clean
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@app.route('/')
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def index():
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"""主页"""
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return render_template('index.html', timeframes=TIMEFRAMES, symbols=SYMBOLS)
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return render_template('index.html',
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timeframes=TIMEFRAMES,
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symbols=SYMBOLS,
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a_stock_symbols=A_STOCK_SYMBOLS)
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@app.route('/api/analyze')
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def analyze():
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@@ -448,7 +576,7 @@ def analyze():
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# 添加主周期分析结果到返回数据
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result.update({
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'kline_data': df.to_dict('records'),
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'kline_data': clean_dataframe_for_json(df).to_dict('records'),
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'bi_list': [{
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'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
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'end_time': (bi.end_klc.end_time if isinstance(bi.end_klc.end_time, str) else bi.end_klc.end_time.astimezone(client_tz).isoformat()) if bi.end_klc else None,
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@@ -522,7 +650,7 @@ def analyze():
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} for bi in element_analysis['bi_list'] if bi.end_klc]
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# 添加小周期K线数据
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result['element_kline_data'] = element_df.to_dict('records')
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result['element_kline_data'] = clean_dataframe_for_json(element_df).to_dict('records')
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result['element_seg_list'] = [{
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'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
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@@ -584,5 +712,59 @@ def get_symbols():
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/a_stocks')
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def get_a_stocks():
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"""获取A股股票列表"""
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try:
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stock_list = china_stock.get_stock_list()
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return jsonify(stock_list)
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/popular_a_stocks')
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def get_popular_a_stocks():
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"""获取热门A股股票"""
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try:
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return jsonify(china_stock.get_popular_stocks())
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/sectors')
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def get_sectors():
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"""获取所有行业分类"""
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try:
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sectors = china_stock.get_all_sectors()
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return jsonify(sectors)
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/stocks_by_sector')
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def get_stocks_by_sector():
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"""根据行业获取股票"""
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try:
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sector = request.args.get('sector')
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if sector:
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stocks = china_stock.get_stock_by_sector(sector)
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return jsonify(stocks)
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else:
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# 返回所有行业的股票分组
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all_sectors = china_stock.get_stock_by_sector()
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return jsonify(all_sectors)
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/search_stock')
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def search_stock():
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"""搜索股票 - 增强版"""
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try:
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keyword = request.args.get('keyword', '')
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if not keyword:
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return jsonify({'error': '搜索关键词不能为空'})
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results = china_stock.search_stock(keyword)
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return jsonify(results)
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except Exception as e:
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return jsonify({'error': str(e)})
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if __name__ == '__main__':
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app.run(debug=True, host='0.0.0.0', port=8123)
|
||||
Reference in New Issue
Block a user