refactor: 缠论引擎包化与 Web 分层(ECR-001)

将根目录引擎迁入 chanlun/ 并保留兼容 shim;拆分 TF_DF 与 web 服务;
前端模块化;strategies 改用 chanlun 导入;补充 ESS 文档与 golden 回归。

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
2026-08-05 18:48:20 +08:00
co-authored by Cursor
parent e2e45bc1bc
commit 74dec4e50b
160 changed files with 25576 additions and 23104 deletions
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"""分析 API。"""
from flask import Blueprint, jsonify, request
from services.runtime import * # noqa: F403
from services import runtime as R
bp = Blueprint("analyze", __name__)
@bp.route('/api/analyze')
def analyze():
"""分析接口"""
symbol = request.args.get('symbol', 'SOL/USDT:USDT')
timeframe = request.args.get('timeframe', '5m')
# 验证交易对不为空
if not symbol or symbol.strip() == '':
return jsonify({'error': '交易对不能为空'})
# 获取时间范围参数
start_time = request.args.get('start_time')
end_time = request.args.get('end_time')
# 获取客户端请求的时区
client_timezone = request.args.get('timezone', 'Asia/Shanghai')
# 获取分形元素时间周期与次次周期
element_timeframe = request.args.get('element_timeframe')
sub_sub_timeframe = request.args.get('sub_sub_timeframe')
# 获取是否只需要分形元素数据的参数
elements_only_param = request.args.get('elements_only')
elements_only = elements_only_param == 'true'
# 验证小周期是否小于主周期
if element_timeframe and not is_smaller_or_equal_timeframe(element_timeframe, timeframe):
return jsonify({'error': '分形元素时间周期必须小于或等于主图表时间周期'})
# 验证次次周期是否小于等于次周期
if sub_sub_timeframe and element_timeframe and not is_smaller_or_equal_timeframe(sub_sub_timeframe, element_timeframe):
return jsonify({'error': '次次周期必须小于或等于次周期'})
# 获取数据
df = get_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
if df is None:
return jsonify({'error': '获取数据失败'})
if len(df) == 0:
return jsonify({'error': '所选时间范围内没有数据'})
# 使用客户端指定的时区
client_tz = timezone(client_timezone)
# 如果只需要分形元素数据而不需要主周期数据,则初始化一个空结果
result = {
'timezone': client_timezone
}
# 如果不是只需要分形元素数据,则添加主周期数据
if not elements_only:
# 添加技术指标(包括布林带)
df = add_indicators(df)
# 进行缠论分析
analysis_result = analyze_chan(df, symbol, timeframe)
# 计算MACD
macd_data = calculate_macd(df)
# 基于已有 KLC 列表生成趋势标记(不做额外计算)
klc_trend = []
try:
for klc in analysis_result.get('klc_list', []):
trend_val = getattr(klc, 'trend', None)
t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
if trend_val is None or t_obj is None:
continue
# 统一成字符串:UP/DOWN/FLAT/UNKNOWN
trend_name = str(trend_val)
if '.' in trend_name:
trend_name = trend_name.split('.')[-1]
time_str = format_time_safely(t_obj, client_tz)
if time_str:
klc_trend.append({'time': time_str, 'trend': trend_name})
except Exception:
klc_trend = []
# 添加主周期分析结果到返回数据
result.update({
'kline_data': clean_dataframe_for_json(df).to_dict('records'),
'klc_list': [{
'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
'open': float(klc.open),
'high': float(klc.high),
'low': float(klc.low),
'close': float(klc.close),
'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
} for klc in analysis_result['klc_list'] if hasattr(klc, 'end_time') and klc.end_time],
'bi_list': [{
'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(),
'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,
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in analysis_result['bi_list'] if bi.is_sure],
# 添加未完成笔列表
'uncompleted_bi_list': [{
'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(),
'end_time': bi.end_time, # 未完成笔没有结束时间
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high, # 未完成笔没有结束价格
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in analysis_result['bi_list'] if not bi.is_sure],
'seg_list': [{
'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(),
'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
'direction': convert_direction(seg.dir)
} for seg in analysis_result['seg_list'] if seg.is_sure],
# 添加未完成线段列表
'uncompleted_seg_list': get_uncompleted_seg_list(analysis_result['seg_list'], client_tz),
'zs_list': [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': zs.is_sure # 添加中枢是否完成的标志
} for zs in analysis_result['zs_list'] if zs.is_sure],
# 添加主周期BI中枢列表(已完成)
'bi_zs_list': [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in analysis_result.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)],
# 添加未完成中枢列表
'uncompleted_zs_list': [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': None, # 未完成中枢没有结束时间
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': zs.is_sure # 未完成中枢的is_sure为False
} for zs in analysis_result['zs_list'] if not zs.is_sure],
# 添加未完成BI中枢列表
'uncompleted_bi_zs_list': [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in analysis_result.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)],
'macd': macd_data,
# 添加布林带数据
'bollinger': {
'upper': df['bb_upper'].tolist(),
'middle': df['bb_middle'].tolist(),
'lower': df['bb_lower'].tolist()
},
'element_bollinger': {
'upper': df['element_bb_upper'].tolist(),
'middle': df['element_bb_middle'].tolist(),
'lower': df['element_bb_lower'].tolist()
},
# 添加ATR数据
'atr': df['atr'].tolist(),
# 添加K线分型信息
'klc_fx_info': [{
'time': format_time_safely(point['time'], client_tz),
'start_time': format_time_safely(point['start_time'], client_tz),
'end_time': format_time_safely(point['end_time'], client_tz),
'price': float(point['price']),
'fx_type': point['fx_type'],
'is_bottom': bool(point['is_bottom']),
'fx_strength': float(point['fx_strength']), # 分型强度分数
'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级
'is_strong_fx': bool(point['is_strong_fx']), # 是否为强分型
# 分型框(虚线矩形)用到的高低价
'high': float(point['high']) if point.get('high') is not None else None,
'low': float(point['low']) if point.get('low') is not None else None
} for point in analysis_result['klc_fx_info']],
# 添加ChanMACD分析数据
'chan_macd': serialize_chan_macd_data(analysis_result.get('chan_macd', {}), client_tz),
# 添加多时间周期EMA52数据
'ema52_dict': analysis_result.get('ema52_dict', {}),
# 直接输出KLC趋势标记(使用已有trend字段)
'klc_trend': klc_trend,
# 添加主周期买卖点列表
# 注意:部分枚举在转为字符串时可能形如 "Chan_BSP_TYPE.BSP1(1)"
# 这里进行健壮的解析,确保前端拿到的始终是 "BSP1" / "BUY" 这种简洁形式,
# 以便与前端的 BSP_STYLE 键(如 "BSP1_BUY")正确匹配。
'bsp_list': [{
'time': format_time_safely(bsp.end_time, client_tz),
'price': float(bsp.klc.low if 'BUY' in str(bsp.dir) else bsp.klc.high),
# -- 规范化 type 名称,例如:
# "Chan_BSP_TYPE.BSP1" -> "BSP1"
# "Chan_BSP_TYPE.BSP1(1)" -> "BSP1"
# "BSP1" -> "BSP1"
'type': (
lambda raw: (
(raw.split('.')[-1] if '.' in raw else raw).split('(')[0]
)
)(str(bsp.type)),
# -- 规范化 dir 名称,例如:
# "Chan_BSP_DIR.BUY" -> "BUY"
# "Chan_BSP_DIR.BUY(1)" -> "BUY"
# "BUY" -> "BUY"
'dir': (
lambda raw: (
(raw.split('.')[-1] if '.' in raw else raw).split('(')[0]
)
)(str(bsp.dir)),
'is_sure': bool(bsp.is_sure),
'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
} for bsp in analysis_result.get('bsp_list', [])]
})
# 如果有指定分形元素时间周期,获取小周期数据
if element_timeframe:
# 获取小周期数据,使用与主周期相同的时间范围
element_df = get_kl_data(symbol, element_timeframe, start_time=start_time, end_time=end_time)
if element_df is not None and len(element_df) > 0:
# 添加小周期技术指标(包括布林带)
element_df = add_indicators(element_df)
# 对小周期数据进行缠论分析
element_analysis = analyze_chan(element_df, symbol, element_timeframe)
# 计算小周期MACD数据
element_macd_data = calculate_macd(element_df)
# 组装小周期 KLC 趋势(仅提取已有 trend,不做重算)
try:
element_klc_trend = []
for klc in element_analysis.get('klc_list', []):
trend_val = getattr(klc, 'trend', None)
t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
if trend_val is None or t_obj is None:
continue
trend_name = str(trend_val)
if '.' in trend_name:
trend_name = trend_name.split('.')[-1]
time_str = format_time_safely(t_obj, client_tz)
if time_str:
element_klc_trend.append({'time': time_str, 'trend': trend_name})
except Exception:
element_klc_trend = []
# 添加小周期分析结果到返回数据
result['element_timeframe'] = element_timeframe
result['element_macd'] = element_macd_data # 添加小周期MACD数据
# 添加小周期布林带数据
result['element_bollinger'] = {
'upper': element_df['bb_upper'].tolist(),
'middle': element_df['bb_middle'].tolist(),
'lower': element_df['bb_lower'].tolist()
}
result['element_element_bollinger'] = {
'upper': element_df['element_bb_upper'].tolist(),
'middle': element_df['element_bb_middle'].tolist(),
'lower': element_df['element_bb_lower'].tolist()
}
# 添加小周期ATR数据
result['element_atr'] = element_df['atr'].tolist()
result['element_bi_list'] = [{
'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(),
'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,
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in element_analysis['bi_list'] if bi.is_sure]
# 添加次周期未完成笔列表
result['element_uncompleted_bi_list'] = [{
'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(),
'end_time': bi.end_time, # 未完成笔没有结束时间
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high, # 未完成笔没有结束价格
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in element_analysis['bi_list'] if not bi.is_sure]
# 添加小周期K线数据
result['element_kline_data'] = clean_dataframe_for_json(element_df).to_dict('records')
# 添加小周期KLC列表
result['element_klc_list'] = [{
'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
'open': float(klc.open),
'high': float(klc.high),
'low': float(klc.low),
'close': float(klc.close),
'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
} for klc in element_analysis['klc_list'] if hasattr(klc, 'end_time') and klc.end_time]
result['element_seg_list'] = [{
'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(),
'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
'direction': convert_direction(seg.dir)
} for seg in element_analysis['seg_list'] if seg.is_sure]
# 添加次周期未完成线段列表
result['element_uncompleted_seg_list'] = get_uncompleted_seg_list(element_analysis['seg_list'], client_tz)
result['element_zs_list'] = [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': zs.is_sure # 添加中枢是否完成的标志
} for zs in element_analysis['zs_list'] if zs.end_klc]
result['element_uncompleted_zs_list'] = [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': None, # 未完成中枢没有结束时间
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': zs.is_sure # 未完成中枢的is_sure为False
} for zs in element_analysis['zs_list'] if not zs.is_sure]
# 添加次周期 BI 中枢(已完成/未完成)
result['element_bi_zs_list'] = [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in element_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
result['element_uncompleted_bi_zs_list'] = [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': None,
'zg': zs.zg,
'zd': zs.zd,
'gg': zs.gg,
'dd': zs.dd,
'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in element_analysis.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)]
# 添加小周期分型信息
result['element_klc_fx_info'] = [{
'time': format_time_safely(point['time'], client_tz),
'start_time': format_time_safely(point['start_time'], client_tz),
'end_time': format_time_safely(point['end_time'], client_tz),
'price': float(point['price']),
'fx_type': point['fx_type'],
'is_bottom': bool(point['is_bottom']),
'fx_strength': float(point['fx_strength']), # 分型强度分数
'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级
'is_strong_fx': bool(point['is_strong_fx']), # 是否为强分型
# 分型框(虚线矩形)用到的高低价
'high': float(point['high']) if point.get('high') is not None else None,
'low': float(point['low']) if point.get('low') is not None else None
} for point in element_analysis['klc_fx_info']]
# 添加次周期ChanMACD分析数据
result['element_chan_macd'] = serialize_chan_macd_data(element_analysis.get('chan_macd', {}), client_tz)
# 添加小周期 KLC 趋势标记
result['element_klc_trend'] = element_klc_trend
# 添加次周期买卖点列表
result['element_bsp_list'] = [{
'time': format_time_safely(bsp.end_time, client_tz),
'price': float(bsp.klc.low if str(bsp.dir) == 'Chan_BSP_DIR.BUY' else bsp.klc.high),
'type': str(bsp.type).replace('Chan_BSP_TYPE.', ''),
'dir': str(bsp.dir).replace('Chan_BSP_DIR.', ''),
'is_sure': bool(bsp.is_sure),
'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
} for bsp in element_analysis.get('bsp_list', [])]
# 次次周期:仅当已指定次周期且次次周期有效时获取
if sub_sub_timeframe and is_smaller_or_equal_timeframe(sub_sub_timeframe, element_timeframe):
sub_sub_df = get_kl_data(symbol, sub_sub_timeframe, start_time=start_time, end_time=end_time)
if sub_sub_df is not None and len(sub_sub_df) > 0:
sub_sub_df = add_indicators(sub_sub_df)
sub_sub_analysis = analyze_chan(sub_sub_df, symbol, sub_sub_timeframe)
result['sub_sub_timeframe'] = sub_sub_timeframe
result['sub_sub_kline_data'] = clean_dataframe_for_json(sub_sub_df).to_dict('records')
result['sub_sub_atr'] = sub_sub_df['atr'].tolist()
result['sub_sub_macd'] = calculate_macd(sub_sub_df)
result['sub_sub_bi_list'] = [{
'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(),
'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,
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in sub_sub_analysis['bi_list'] if bi.is_sure]
result['sub_sub_uncompleted_bi_list'] = [{
'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(),
'end_time': bi.end_time,
'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
'end_price': bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high,
'direction': convert_direction(bi.dir),
'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
} for bi in sub_sub_analysis['bi_list'] if not bi.is_sure]
# 次次周期 KLC 列表
result['sub_sub_klc_list'] = [{
'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
'open': float(klc.open),
'high': float(klc.high),
'low': float(klc.low),
'close': float(klc.close),
'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
} for klc in sub_sub_analysis.get('klc_list', []) if hasattr(klc, 'end_time') and klc.end_time]
result['sub_sub_seg_list'] = [{
'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(),
'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
'direction': convert_direction(seg.dir)
} for seg in sub_sub_analysis['seg_list'] if seg.is_sure]
result['sub_sub_uncompleted_seg_list'] = get_uncompleted_seg_list(sub_sub_analysis['seg_list'], client_tz)
result['sub_sub_zs_list'] = [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': zs.is_sure
} for zs in sub_sub_analysis['zs_list'] if zs.end_klc]
result['sub_sub_uncompleted_zs_list'] = [{
'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
'end_time': None, 'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': zs.is_sure
} for zs in sub_sub_analysis['zs_list'] if not zs.is_sure]
result['sub_sub_bi_zs_list'] = [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in sub_sub_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
result['sub_sub_uncompleted_bi_zs_list'] = [{
'start_time': (
(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
if getattr(zs.start_klc, 'end_time', None) else
(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
),
'end_time': None, 'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in sub_sub_analysis.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)]
result['sub_sub_klc_fx_info'] = [{
'time': format_time_safely(point['time'], client_tz),
'start_time': format_time_safely(point['start_time'], client_tz),
'end_time': format_time_safely(point['end_time'], client_tz),
'price': float(point['price']),
'fx_type': point['fx_type'],
'is_bottom': bool(point['is_bottom']),
'fx_strength': float(point['fx_strength']),
'fx_strength_level': str(point['fx_strength_level']),
'is_strong_fx': bool(point['is_strong_fx']),
# 分型框(虚线矩形)用到的高低价
'high': float(point['high']) if point.get('high') is not None else None,
'low': float(point['low']) if point.get('low') is not None else None
} for point in sub_sub_analysis['klc_fx_info']]
result['sub_sub_bsp_list'] = [{
'time': format_time_safely(bsp.end_time, client_tz),
'price': float(bsp.klc.low if str(bsp.dir) == 'Chan_BSP_DIR.BUY' else bsp.klc.high),
'type': str(bsp.type).replace('Chan_BSP_TYPE.', ''),
'dir': str(bsp.dir).replace('Chan_BSP_DIR.', ''),
'is_sure': bool(bsp.is_sure),
'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
} for bsp in sub_sub_analysis.get('bsp_list', [])]
result['sub_sub_chan_macd'] = serialize_chan_macd_data(sub_sub_analysis.get('chan_macd', {}), client_tz)
try:
sub_sub_klc_trend = []
for klc in sub_sub_analysis.get('klc_list', []):
trend_val = getattr(klc, 'trend', None)
t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
if trend_val is None or t_obj is None:
continue
trend_name = str(trend_val)
if '.' in trend_name:
trend_name = trend_name.split('.')[-1]
time_str = format_time_safely(t_obj, client_tz)
if time_str:
sub_sub_klc_trend.append({'time': time_str, 'trend': trend_name})
result['sub_sub_klc_trend'] = sub_sub_klc_trend
except Exception:
result['sub_sub_klc_trend'] = []
pass
# 结构价值区分析(Structure Zone)—— 按需拉取:仅当 include_structure_zones 为真时执行多周期拉取(默认跳过以减轻负载)
include_zones_param = request.args.get('include_structure_zones', '')
include_structure_zones = str(include_zones_param).lower() in ('1', 'true', 'yes')
if include_structure_zones:
zone_timeframes_str = request.args.get('zone_timeframes', '')
zone_kl_lines = int(request.args.get('zone_kl_lines', 1000))
try:
zone_config = StructureZoneConfig(kl_lines_per_tf=zone_kl_lines)
if zone_timeframes_str:
zone_config.zone_timeframes = [t.strip() for t in zone_timeframes_str.split(',') if t.strip()]
analyses = {}
ema52_dict = {}
latest_close = 0.0
now = time.time()
def _fetch_single_tf_zone(tf_name):
"""单个时间周期的结构区数据拉取(线程安全)"""
cache_key = f"{symbol}:{tf_name}:{zone_kl_lines}"
cached = _zone_cache.get(cache_key)
if cached and cached['expires'] > now:
print(f" 结构区缓存命中: {tf_name}")
return {
'tf_name': tf_name,
'analyses': cached['analyses'],
'ema52': cached['ema52'],
'close': cached.get('close', 0.0),
'cached': True,
}
try:
tf_df = get_kl_data(symbol, tf_name, limit=zone_kl_lines)
if tf_df is None or len(tf_df) == 0:
return None
tf_df = add_indicators(tf_df)
tf_analysis = analyze_chan(tf_df, symbol, tf_name)
zs_serialized = [{
'start_time': (zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat()) if zs.start_klc else None,
'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd,
'is_sure': zs.is_sure
} for zs in tf_analysis.get('zs_list', []) if zs.is_sure]
bi_zs_serialized = [{
'start_time': ((zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat()) if getattr(zs.start_klc, 'end_time', None) else (zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())),
'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd,
'is_sure': bool(getattr(zs, 'is_sure', False))
} for zs in tf_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
last_ema = tf_df['ema52'].iloc[-1] if 'ema52' in tf_df.columns else 0
ema_val = float(last_ema) if last_ema and last_ema > 0 else None
last_close = float(tf_df['close'].iloc[-1])
tf_result = {
'tf_name': tf_name,
'analyses': {'zs_list': zs_serialized, 'bi_zs_list': bi_zs_serialized},
'ema52': ema_val,
'close': last_close,
'cached': False,
}
# 写入缓存
_zone_cache[cache_key] = {
'analyses': tf_result['analyses'],
'ema52': ema_val,
'close': last_close,
'expires': now + _zone_cache_ttl(tf_name),
}
print(f" 结构区数据: {tf_name} -> zs={len(zs_serialized)}, bi_zs={len(bi_zs_serialized)}, ema52={ema_val}")
return tf_result
except Exception as e:
print(f" 结构区 {tf_name} 拉取失败: {e}")
return None
with ThreadPoolExecutor(max_workers=len(zone_config.zone_timeframes)) as executor:
futures = {executor.submit(_fetch_single_tf_zone, tf): tf for tf in zone_config.zone_timeframes}
for future in as_completed(futures):
tf_result = future.result()
if tf_result is None:
continue
tf_name = tf_result['tf_name']
analyses[tf_name] = tf_result['analyses']
ema52_dict[tf_name] = tf_result['ema52']
if tf_result['close'] and (not latest_close or latest_close == 0.0):
latest_close = tf_result['close']
structure_zones = analyze_structure_zones_from_serialized(
analyses, ema52_dict, latest_close, config=zone_config
)
result['structure_zones'] = [{
'id': z.id,
'lower': z.lower,
'upper': z.upper,
'center': z.center,
'width_pct': z.width_pct,
'zone_type': z.zone_type,
'timeframes': z.timeframes,
'structure_types': z.structure_types,
'boundary_types': z.boundary_types,
'overlap_count': z.overlap_count,
'touch_count': z.touch_count,
'recency_score': z.recency_score,
'ema52_distance_pct': z.ema52_distance_pct,
'ema52_aligned': z.ema52_aligned,
'strength_score': z.strength_score,
'confidence': z.confidence,
'first_seen': z.first_seen,
'last_seen': z.last_seen,
'metadata': z.metadata,
} for z in structure_zones]
except Exception as e:
print(f"StructureZone 分析出错: {e}")
import traceback
traceback.print_exc()
result['structure_zones'] = []
else:
result['structure_zones'] = []
return jsonify(result)