refactor: ECR-002 拆分 runtime 包并加深 analyze 契约（已审）
将 web/services/runtime.py 拆为 runtime/ 子模块并保持门面兼容；补齐 ESS 文档、门面/契约/TF_DF 测试与 CODE_REVIEW Approve。 Co-authored-by: Cursor <cursoragent@cursor.com>
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"""runtime 门面：保持 `from services.runtime import *` 与 `import services.runtime as R` 兼容。"""
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from __future__ import annotations
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# ---- 历史兼容：旧 monolith 上 `from pytz import timezone` 等会随 import * 漏出 ----
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import json # noqa: F401
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import logging
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import sys as _sys
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import time # noqa: F401
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from collections import OrderedDict # noqa: F401
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from concurrent.futures import ThreadPoolExecutor, as_completed # noqa: F401
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import numpy as np # noqa: F401
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from pytz import timezone # noqa: F401
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from chanlun.analysis.ChanZone import ( # noqa: F401
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StructureZoneConfig,
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analyze_structure_zones_from_serialized,
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)
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logger = logging.getLogger("services.runtime")
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from .state import ( # noqa: F401
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TRADE_POINT_TYPE,
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macd_fast_period,
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macd_slow_period,
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macd_signal_period,
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exchange,
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china_stock,
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_zone_cache,
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DEFAULT_TIMEFRAME_LABELS,
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DEFAULT_SYMBOLS,
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TIMEFRAMES,
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SYMBOLS,
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DATA_SERVICE_AVAILABLE,
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SERVICE_METADATA_LAST_REFRESH,
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)
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from .timeframes import ( # noqa: F401
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_zone_cache_ttl,
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timeframe_to_minutes,
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format_timeframe_label,
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build_timeframe_labels,
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compute_timeframe_defaults,
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is_smaller_timeframe,
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is_smaller_or_equal_timeframe,
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)
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from .market_data import ( # noqa: F401
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_parse_time_input,
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refresh_data_service_metadata,
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_fetch_kl_from_datasvc,
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A_STOCK_SYMBOLS,
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detect_symbol_type,
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get_kl_data,
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_get_crypto_kl_data_via_ccxt,
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get_crypto_kl_data,
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get_a_stock_kl_data,
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load_crypto_symbols,
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)
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from .indicators import ( # noqa: F401
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add_indicators,
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calculate_macd,
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)
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from .analyze import ( # noqa: F401
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analyze_chan,
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classify_trend_stage,
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)
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from .serialize import ( # noqa: F401
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convert_direction,
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format_time_safely,
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serialize_chan_macd_data,
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clean_dataframe_for_json,
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get_uncompleted_seg_list,
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)
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# 预取元信息（与拆分前模块加载行为一致）
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refresh_data_service_metadata(force=True)
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# 标量在 import 时会拷贝；刷新后写回本模块，供 `from services.runtime import *` 读到最新值
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from . import state as _state
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_mod = _sys.modules[__name__]
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_mod.DATA_SERVICE_AVAILABLE = _state.DATA_SERVICE_AVAILABLE
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_mod.SERVICE_METADATA_LAST_REFRESH = _state.SERVICE_METADATA_LAST_REFRESH
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_mod.macd_fast_period = _state.macd_fast_period
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_mod.macd_slow_period = _state.macd_slow_period
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_mod.macd_signal_period = _state.macd_signal_period
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def __getattr__(name: str):
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if hasattr(_state, name):
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return getattr(_state, name)
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raise AttributeError(name)
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def __dir__():
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return sorted(set(globals()) | set(dir(_state)))
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from __future__ import annotations
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import numpy as np
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import talib.abstract as ta
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from chanlun import TF_DF
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from chanlun.core.ChanEnum import Chan_KLC_FX, Chan_FX_TYPE
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from chanlun.indicators.ChanMACD import ChanMACD
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from .indicators import calculate_macd
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def analyze_chan(df, symbol=None, timeframe=None):
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"""进行缠论分析"""
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chan = TF_DF()
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# 初始化多时间周期数据以获取EMA52
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ema52_dict = None
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# 获取分析结果
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klu_list = chan.get_kl_data(df)
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klc_list = chan.get_klc_list(klu_list)
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bi_list = chan.cal_bi_list(klc_list)
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#for index in range(0, 10):
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#print(bi_list[index].start_time, bi_list[index].start_klc.end_time, bi_list[index].dir)
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seg_list = chan.get_seg_list(bi_list)
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zs_list = chan.calculate_seg_zs(seg_list)
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# 计算笔中枢（BI中枢）并拍平成列表
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#bi_zs_list = chan.cal_bi_zs_list_pure(bi_list)
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bi_zs_list = chan.cal_bi_zs(seg_list)
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bsp_list = []
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if len(bi_zs_list) > 0:
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bsp_list = chan.find_all_bsp(bi_list, bi_zs_list)
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#bsp_state_list = chan.get_bsp_state(df)
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#for bsp in bsp_list:
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#print(bsp.end_time, bsp.type, bsp.dir)
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# 添加买卖点识别
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for bi in bi_list:
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bi.cal_macdhist()
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for bi in bi_list:
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bi.cal_macd_div()
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#print(bi.start_time, bi.macd_hist, bi.macd_div)
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# 添加ChanMACD分析（复用 get_klc_list 内已算好的结果，避免同周期二次全量分析）
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chan_macd = None
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chan_macd_data = {}
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try:
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if klu_list and len(klu_list) > 0:
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print(f"获取到KLU列表，长度: {len(klu_list)}")
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chan_macd = getattr(chan, '_last_chan_macd', None)
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if chan_macd is None:
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chan_macd = ChanMACD(klu_list)
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chan_macd_data = {
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'seg_list': chan_macd.seg_list,
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'unittf_list': chan_macd.unittf_list,
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'histset_list': chan_macd.histset_list,
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'klu_list': chan_macd.klu_list,
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'high_position_list': chan_macd.high_position_list,
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'high_empty_list': chan_macd.high_empty_list,
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'low_position_list': getattr(chan_macd, 'low_position_list', []),
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'low_empty_list': getattr(chan_macd, 'low_empty_list', []),
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'return_zero_list': chan_macd.return_zero_list,
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'cross0_up_list': chan_macd.cross0_up_list,
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'cross0_down_list': chan_macd.cross0_down_list
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}
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print(f"ChanMACD分析完成: seg={len(chan_macd.seg_list)}, unittf={len(chan_macd.unittf_list)}, histset={len(chan_macd.histset_list)}")
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else:
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print("未能获取KLU列表或列表为空")
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chan_macd_data = {
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'seg_list': [],
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'unittf_list': [],
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'histset_list': [],
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'high_position_list': [],
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'high_empty_list': [],
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'return_zero_list': [],
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'cross0_up_list': [],
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'cross0_down_list': []
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}
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except Exception as e:
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print(f"ChanMACD分析出错: {e}")
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import traceback
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traceback.print_exc()
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chan_macd_data = {
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'seg_list': [],
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'unittf_list': [],
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'histset_list': [],
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'high_position_list': [],
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'high_empty_list': [],
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'low_position_list': [],
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'low_empty_list': [],
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'return_zero_list': [],
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'cross0_up_list': [],
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'cross0_down_list': []
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}
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# 提取K线分型信息
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klc_fx_info = []
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for klc in klc_list:
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if hasattr(klc, 'klc_fx_type') and klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
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try:
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# 计算分型强度
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fx_strength = 0
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fx_strength_level = ""
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is_strong_fx = False
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# 统一使用cal_fx_strength函数
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if hasattr(klc, 'cal_fx_strength'):
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fx_strength = klc.cal_fx_strength(5)
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# 尝试获取分型强度等级
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if hasattr(klc, 'get_fx_strength_level'):
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fx_strength_level = klc.get_fx_strength_level()
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# 尝试判断是否为强分型
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if hasattr(klc, 'is_strong_fx'):
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is_strong_fx = klc.is_strong_fx()
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# 如果分型强度小于1，设为0
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if fx_strength < 1:
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fx_strength = 0
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# KLC 分型框（起止时间+高低价）：
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# 仅使用 cal_fx_box 通过 display 条件后生成的 klc.fx_box。
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# 若无 fx_box，则前端不应绘制分型框。
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fx_box = getattr(klc, 'fx_box', None)
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box_start_time = getattr(fx_box, 'start_time', None) if fx_box else None
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box_end_time = getattr(fx_box, 'end_time', None) if fx_box else None
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box_high = getattr(fx_box, 'high', None) if fx_box else None
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box_low = getattr(fx_box, 'low', None) if fx_box else None
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if klc.bb_out:
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klc_fx_info.append({
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'time': klc.end_time,
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'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
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'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
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'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
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'fx_strength': fx_strength, # 分型强度分数 (0-100)
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'fx_strength_level': fx_strength_level, # 分型强度等级 (极强/强/中等/弱/极弱)
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'is_strong_fx': is_strong_fx, # 是否为强分型
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# 虚线分型框信息（给前端画框用）
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'start_time': box_start_time,
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'end_time': box_end_time,
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'high': float(box_high) if box_high is not None else None,
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'low': float(box_low) if box_low is not None else None,
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})
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except Exception as e:
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# 如果出错，仍然添加基本信息，但分型强度为0
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fx_box = getattr(klc, 'fx_box', None)
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box_start_time = getattr(fx_box, 'start_time', None) if fx_box else None
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box_end_time = getattr(fx_box, 'end_time', None) if fx_box else None
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box_high = getattr(fx_box, 'high', None) if fx_box else None
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box_low = getattr(fx_box, 'low', None) if fx_box else None
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klc_fx_info.append({
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'time': klc.end_time,
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'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
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'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
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'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
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'fx_strength': 0,
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'fx_strength_level': "",
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'is_strong_fx': False,
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# 虚线分型框信息（给前端画框用）
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'start_time': box_start_time,
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'end_time': box_end_time,
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'high': float(box_high) if box_high is not None else None,
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'low': float(box_low) if box_low is not None else None,
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})
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return {
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'klc_list': klc_list,
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'klu_list': klu_list, # 添加KLU列表
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'bi_list': bi_list,
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'seg_list': seg_list,
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'zs_list': zs_list,
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'bi_zs_list': bi_zs_list, # 添加BI中枢列表
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'bsp_list': bsp_list, # 添加买卖点列表
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'klc_fx_info': klc_fx_info, # KLC分型信息
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'chan_macd': chan_macd_data, # 添加ChanMACD分析数据
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'ema52_dict': ema52_dict # 添加多时间周期EMA52数据
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}
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def classify_trend_stage(df):
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"""根据 EMA 斜率与多空排列判断趋势方向与阶段
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返回: direction in {"bull","bear","sideways"}, stage in {"early","mid","late"}, strength_score (0-100)
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"""
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if df is None or len(df) < 60:
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return "sideways", "early", 0
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# 使用 EMA5/10/24/52
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closes = df['close'].values
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ema5 = df['ema5'].values if 'ema5' in df else ta.EMA(df, timeperiod=5)
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ema10 = df['ema10'].values if 'ema10' in df else ta.EMA(df, timeperiod=10)
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ema24 = df['ema24'].values if 'ema24' in df else ta.EMA(df, timeperiod=24)
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ema52 = df['ema52'].values if 'ema52' in df else ta.EMA(df, timeperiod=52)
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# 最近N根用于斜率与排列判定
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lookback = min(30, len(df) - 1)
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if lookback <= 5:
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return "sideways", "early", 0
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# 简单斜率: 最近k根的线性变化率近似
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def slope(arr, k=10):
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k = min(k, len(arr) - 1)
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if k < 2:
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return 0.0
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y = arr[-k:]
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x = np.arange(k)
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# 最小二乘拟合斜率
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denom = np.dot(x - x.mean(), x - x.mean())
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if denom == 0:
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return 0.0
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m = np.dot(y - y.mean(), x - x.mean()) / denom
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return float(m)
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k_slope = 12 # 斜率窗口
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s5 = slope(ema5, k_slope)
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s10 = slope(ema10, k_slope)
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s24 = slope(ema24, k_slope)
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s52 = slope(ema52, k_slope)
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# 多空排列
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last5, last10, last24, last52 = ema5[-1], ema10[-1], ema24[-1], ema52[-1]
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bull_stack = last5 > last10 > last24 > last52
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bear_stack = last5 < last10 < last24 < last52
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# 波动性与动量增强: MACD 柱体最近均值
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macdhist = df['macdhist'].values if 'macdhist' in df else calculate_macd(df)['histogram']
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hist_recent = macdhist[-lookback:]
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hist_power = float(np.mean(np.abs(hist_recent))) if len(hist_recent) else 0.0
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# 方向
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if bull_stack and s24 > 0 and s52 > 0:
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direction = "bull"
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elif bear_stack and s24 < 0 and s52 < 0:
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direction = "bear"
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else:
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# 用价格相对 EMA52 辅助
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if closes[-1] > last52 and (s24 + s52) > 0:
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direction = "bull"
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elif closes[-1] < last52 and (s24 + s52) < 0:
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direction = "bear"
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else:
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direction = "sideways"
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# 阶段: 依据（斜率大小、与EMA52距离、MACD柱体扩张/收敛）
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dist52 = float((closes[-1] - last52) / last52) if last52 else 0.0
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slope_score = max(0.0, (abs(s24) + abs(s52)) * 1000.0) # 归一化
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dist_score = min(50.0, abs(dist52) * 200.0)
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hist_score = min(30.0, hist_power * 10.0)
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strength = float(min(100.0, slope_score + dist_score + hist_score))
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# 简单阶段判定
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if direction == "sideways":
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stage = "early"
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strength = min(strength, 30.0)
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else:
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# 查看最近 hist 是否在扩大或收敛
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if len(hist_recent) >= 6:
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recent_growth = np.mean(np.abs(hist_recent[-3:])) - np.mean(np.abs(hist_recent[-6:-3]))
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else:
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recent_growth = 0.0
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if recent_growth > 0 and abs(dist52) < 0.05:
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stage = "early"
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elif recent_growth > 0 and abs(dist52) >= 0.05:
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stage = "mid"
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else:
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stage = "late"
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return direction, stage, strength
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@@ -0,0 +1,103 @@
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from __future__ import annotations
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import talib.abstract as ta
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from . import state
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def add_indicators(df):
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macd = ta.MACD(df, fastperiod=state.macd_fast_period, slowperiod=state.macd_slow_period, signalperiod=state.macd_signal_period)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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df['macdhist'] = macd['macdhist']
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df['ma5'] = (ta.MA(df, timeperiod=5)).fillna(0)
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df['ma10'] = (ta.MA(df, timeperiod=10)).fillna(0)
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df['ma30'] = (ta.EMA(df, timeperiod=30)).fillna(0)
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df['ma250'] = (ta.MA(df, timeperiod=250)).fillna(0)
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# 新增 EMA 指标
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df['ema5'] = (ta.EMA(df, timeperiod=5)).fillna(0)
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df['ema10'] = (ta.EMA(df, timeperiod=10)).fillna(0)
|
||||
df['ema24'] = (ta.EMA(df, timeperiod=24)).fillna(0)
|
||||
df['ema52'] = (ta.EMA(df, timeperiod=52)).fillna(0)
|
||||
df['ema26'] = (ta.EMA(df, timeperiod=26)).fillna(0)
|
||||
df['ema13'] = (ta.EMA(df, timeperiod=13)).fillna(0)
|
||||
df['ema7'] = (ta.EMA(df, timeperiod=7)).fillna(0)
|
||||
df['ema104'] = (ta.EMA(df, timeperiod=104)).fillna(0)
|
||||
df['ema156'] = (ta.EMA(df, timeperiod=156)).fillna(0)
|
||||
df['ema208'] = (ta.EMA(df, timeperiod=208)).fillna(0)
|
||||
# 常用SMA 24/52
|
||||
try:
|
||||
df['sma24'] = (ta.SMA(df, timeperiod=24)).fillna(0)
|
||||
df['sma52'] = (ta.SMA(df, timeperiod=52)).fillna(0)
|
||||
except Exception:
|
||||
df['sma24'] = 0
|
||||
df['sma52'] = 0
|
||||
df['rsi'] = ta.RSI(df, timeperiod=14)
|
||||
|
||||
# 计算布林带 (当前周期 - 20周期，2标准差)
|
||||
bb = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
|
||||
df['bb_upper'] = bb['upperband'].fillna(0)
|
||||
df['bb_middle'] = bb['middleband'].fillna(0)
|
||||
df['bb_lower'] = bb['lowerband'].fillna(0)
|
||||
bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
|
||||
#bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
|
||||
df['bbup30'] = bb30['upperband'].fillna(0)
|
||||
df['bblow30'] = bb30['lowerband'].fillna(0)
|
||||
bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
|
||||
#bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
|
||||
df['bbup302'] = bb302['upperband'].fillna(0)
|
||||
df['bblow302'] = bb302['lowerband'].fillna(0)
|
||||
# 计算次周期布林带 (14周期，2标准差)
|
||||
bb_element = ta.BBANDS(df, timeperiod=14, nbdevup=2.0, nbdevdn=2.0, matype=0)
|
||||
df['element_bb_upper'] = bb_element['upperband'].fillna(0)
|
||||
df['element_bb_middle'] = bb_element['middleband'].fillna(0)
|
||||
df['element_bb_lower'] = bb_element['lowerband'].fillna(0)
|
||||
|
||||
df['macd'] = df['macd'].fillna(0)
|
||||
df['macdsignal'] = df['macdsignal'].fillna(0)
|
||||
df['macdhist'] = df['macdhist'].fillna(0)
|
||||
df['ma5'] = df['ma5'].fillna(0)
|
||||
df['ma10'] = df['ma10'].fillna(0)
|
||||
df['ma30'] = df['ma30'].fillna(0)
|
||||
df['ma250'] = df['ma250'].fillna(0)
|
||||
df['ema5'] = df['ema5'].fillna(0)
|
||||
df['ema10'] = df['ema10'].fillna(0)
|
||||
df['ema24'] = df['ema24'].fillna(0)
|
||||
df['ema52'] = df['ema52'].fillna(0)
|
||||
df['sma24'] = df['sma24'].fillna(0)
|
||||
df['sma52'] = df['sma52'].fillna(0)
|
||||
df['rsi'] = df['rsi'].fillna(0)
|
||||
df['avg_volume'] = df['volume'].rolling(10).mean()
|
||||
# 计算量比，避免产生Infinity值
|
||||
df['volume_ratio'] = df['volume'] / df['avg_volume']
|
||||
# 填充缺失值（前N根K线）
|
||||
df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
|
||||
df['avg_volume'] = df['avg_volume'].fillna(0)
|
||||
|
||||
# 处理Infinity和-Infinity值
|
||||
df['volume_ratio'] = df['volume_ratio'].replace([float('inf'), float('-inf')], 1.0)
|
||||
|
||||
# 计算ATR (Average True Range) - 14周期
|
||||
df['atr'] = ta.ATR(df, timeperiod=14)
|
||||
df['atr'] = df['atr'].fillna(0)
|
||||
bb2633 = ta.BBANDS(df, timeperiod=26, nbdevup=3.0, nbdevdn=3.0, matype=0)
|
||||
bbp2633 = (df['close'] - bb2633['lowerband']) / (bb2633['upperband'] - bb2633['lowerband'])
|
||||
df['bb2633upper'] = bb2633['upperband'].fillna(0)
|
||||
df['bb2633lower'] = bb2633['lowerband'].fillna(0)
|
||||
df['bbp2633'] = bbp2633.fillna(0)
|
||||
df['bb2633middle'] = bb2633['middleband'].fillna(0)
|
||||
return df
|
||||
|
||||
def calculate_macd(df):
|
||||
"""计算MACD指标"""
|
||||
exp1 = df['close'].ewm(span=state.macd_fast_period, adjust=False).mean()
|
||||
exp2 = df['close'].ewm(span=state.macd_slow_period, adjust=False).mean()
|
||||
macd = exp1 - exp2
|
||||
signal = macd.ewm(span=state.macd_signal_period, adjust=False).mean()
|
||||
histogram = macd - signal
|
||||
|
||||
return {
|
||||
'macd': macd.tolist(),
|
||||
'signal': signal.tolist(),
|
||||
'histogram': histogram.tolist()
|
||||
}
|
||||
|
||||
@@ -0,0 +1,321 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from config import DATA_SERVICE_URL
|
||||
from . import state
|
||||
from .state import DEFAULT_SYMBOLS, DEFAULT_TIMEFRAME_LABELS
|
||||
from .timeframes import build_timeframe_labels
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def _parse_time_input(value):
|
||||
if value in (None, '', 0):
|
||||
return None
|
||||
try:
|
||||
return int(float(value))
|
||||
except (ValueError, TypeError):
|
||||
return None
|
||||
|
||||
|
||||
def refresh_data_service_metadata(force=False):
|
||||
"""刷新数据服务提供的交易对与周期元信息。"""
|
||||
now = time.time()
|
||||
if not force and state.DATA_SERVICE_AVAILABLE and now - state.SERVICE_METADATA_LAST_REFRESH < 60:
|
||||
return True
|
||||
try:
|
||||
resp = requests.get(f"{DATA_SERVICE_URL}/health", timeout=5)
|
||||
resp.raise_for_status()
|
||||
payload = resp.json()
|
||||
service_symbols = payload.get("symbols") or payload.get("symbol_list") or []
|
||||
base_timeframes = payload.get("timeframes") or payload.get("base_timeframes") or []
|
||||
derived = payload.get("derived_timeframes") or []
|
||||
service_timeframes = list(base_timeframes)
|
||||
for tf in derived:
|
||||
if tf not in service_timeframes:
|
||||
service_timeframes.append(tf)
|
||||
if service_symbols:
|
||||
state.SYMBOLS[:] = service_symbols
|
||||
if service_timeframes:
|
||||
state.TIMEFRAMES.clear()
|
||||
state.TIMEFRAMES.update(build_timeframe_labels(service_timeframes))
|
||||
state.DATA_SERVICE_AVAILABLE = True
|
||||
state.SERVICE_METADATA_LAST_REFRESH = now
|
||||
return True
|
||||
except Exception as exc:
|
||||
logger.warning("无法加载数据服务元信息: %s", exc)
|
||||
if not state.DATA_SERVICE_AVAILABLE:
|
||||
state.TIMEFRAMES.clear()
|
||||
state.TIMEFRAMES.update(DEFAULT_TIMEFRAME_LABELS)
|
||||
state.SYMBOLS[:] = DEFAULT_SYMBOLS
|
||||
state.DATA_SERVICE_AVAILABLE = False
|
||||
return False
|
||||
|
||||
|
||||
def _fetch_kl_from_datasvc(symbol, timeframe, start_ms=None, end_ms=None, limit=None):
|
||||
params = {"symbol": symbol, "tf": timeframe}
|
||||
if start_ms is not None:
|
||||
params["start"] = int(start_ms)
|
||||
if end_ms is not None:
|
||||
params["end"] = int(end_ms)
|
||||
if limit is not None:
|
||||
params["limit"] = limit
|
||||
resp = requests.get(f"{DATA_SERVICE_URL}/api/candles", params=params, timeout=10)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
if not data:
|
||||
return None
|
||||
df = pd.DataFrame(data)
|
||||
if df.empty or "timestamp" not in df.columns:
|
||||
return None
|
||||
numeric_cols = ["open", "high", "low", "close", "volume"]
|
||||
df["timestamp"] = pd.to_numeric(df["timestamp"], errors="coerce")
|
||||
df = df.dropna(subset=["timestamp"])
|
||||
df["timestamp"] = df["timestamp"].astype("int64")
|
||||
for col in numeric_cols:
|
||||
if col in df.columns:
|
||||
df[col] = pd.to_numeric(df[col], errors="coerce")
|
||||
df = df.dropna(subset=numeric_cols)
|
||||
df = df.sort_values("timestamp")
|
||||
if limit and len(df) > limit:
|
||||
df = df.tail(limit)
|
||||
df = df.reset_index(drop=True)
|
||||
df["date"] = pd.to_datetime(df["timestamp"], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai')
|
||||
return df
|
||||
|
||||
|
||||
# 模块加载时尝试预取一次元信息，但失败不阻塞后续流程
|
||||
refresh_data_service_metadata(force=True)
|
||||
|
||||
# A股热门股票
|
||||
# 模板中 A 股下拉仅放默认一项；用户切换到「A股」时由前端请求 /api/a_stocks 填充全市场（约 5500+）
|
||||
A_STOCK_SYMBOLS = [{'symbol': '000001', 'name': '平安银行'}]
|
||||
|
||||
def detect_symbol_type(symbol):
|
||||
"""检测交易对类型：crypto 或 a_stock"""
|
||||
if '/' in symbol and 'USDT' in symbol:
|
||||
return 'crypto'
|
||||
elif len(symbol) == 6 and symbol.isdigit():
|
||||
return 'a_stock'
|
||||
else:
|
||||
return 'unknown'
|
||||
|
||||
def get_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
|
||||
"""获取K线数据，支持加密货币和A股"""
|
||||
symbol_type = detect_symbol_type(symbol)
|
||||
|
||||
if symbol_type == 'crypto':
|
||||
return get_crypto_kl_data(symbol, timeframe, limit, start_time, end_time)
|
||||
elif symbol_type == 'a_stock':
|
||||
return get_a_stock_kl_data(symbol, timeframe, limit, start_time, end_time)
|
||||
else:
|
||||
return None
|
||||
|
||||
def _get_crypto_kl_data_via_ccxt(symbol, timeframe, limit=100000, start_time=None, end_time=None):
|
||||
"""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"""
|
||||
try:
|
||||
# 初始化参数
|
||||
since = None
|
||||
if start_time:
|
||||
try:
|
||||
since = int(start_time)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# 结束时间处理
|
||||
until = None
|
||||
if end_time:
|
||||
try:
|
||||
until = int(end_time)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# 根据时间周期调整每次请求的数据量
|
||||
batch_size = 1000 # 默认批次大小
|
||||
if timeframe in ['1m', '3m', '5m']:
|
||||
batch_size = 1000 # 分钟级数据减少批次大小
|
||||
elif timeframe in ['15m', '30m', '1h']:
|
||||
batch_size = 1000
|
||||
else:
|
||||
batch_size = 1500 # 日线及以上可以获取更多
|
||||
batch_size = 1500 # 默认批次大小
|
||||
# 初始化存储所有K线数据的列表
|
||||
all_ohlcv = []
|
||||
|
||||
# 初始化当前查询的开始时间
|
||||
current_since = since
|
||||
|
||||
# 添加请求计数和最大限制
|
||||
request_count = 0
|
||||
max_requests = 300 # 最大请求次数，防止无限循环
|
||||
|
||||
# 分页加载数据
|
||||
while request_count < max_requests:
|
||||
request_count += 1
|
||||
|
||||
try:
|
||||
# 获取当前页的数据
|
||||
ohlcv = state.exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=batch_size)
|
||||
|
||||
# 如果没有获取到数据，结束循环
|
||||
if not ohlcv or len(ohlcv) == 0:
|
||||
break
|
||||
|
||||
# 将获取到的数据添加到总列表中
|
||||
all_ohlcv.extend(ohlcv)
|
||||
|
||||
# 获取最后一条数据的时间戳
|
||||
last_timestamp = ohlcv[-1][0]
|
||||
|
||||
# 如果已达到结束时间，结束循环
|
||||
if until and last_timestamp >= until:
|
||||
break
|
||||
|
||||
# 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
|
||||
if len(ohlcv) < batch_size:
|
||||
break
|
||||
|
||||
# 更新下一页的开始时间（加1毫秒避免重复）
|
||||
current_since = last_timestamp + 1
|
||||
|
||||
except Exception as e:
|
||||
# 如果单个批次失败，继续尝试下一个批次
|
||||
if current_since:
|
||||
# 尝试增加时间跳过可能的问题时间点
|
||||
current_since += 60000 # 跳过1分钟
|
||||
else:
|
||||
break
|
||||
|
||||
# 防止API请求过于频繁
|
||||
time.sleep(0.3) # 减少到0.3秒提高效率
|
||||
|
||||
# 数据为空的情况
|
||||
if not all_ohlcv or len(all_ohlcv) == 0:
|
||||
return None
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(all_ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
|
||||
df['date'] = pd.to_datetime(df['timestamp'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
|
||||
|
||||
# 在客户端进行结束时间过滤
|
||||
if until:
|
||||
df = df[df['timestamp'] <= until]
|
||||
|
||||
# 去除重复数据
|
||||
df = df.drop_duplicates(subset=['timestamp'])
|
||||
|
||||
# 按时间排序
|
||||
df = df.sort_values('timestamp')
|
||||
|
||||
# 限制数据条数的逻辑 - 优先考虑时间范围
|
||||
if start_time and end_time:
|
||||
# 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
|
||||
if len(df) > 100000: # 防止数据量过大，设置一个合理的上限
|
||||
df = df.tail(100000).reset_index(drop=True)
|
||||
elif limit and len(df) > limit:
|
||||
# 如果没有指定明确时间范围，使用默认的limit限制
|
||||
df = df.tail(limit).reset_index(drop=True)
|
||||
|
||||
# 如果过滤后没有数据，返回None
|
||||
if len(df) == 0:
|
||||
return None
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
|
||||
def get_crypto_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
|
||||
"""优先通过本地数据服务获取加密货币K线，失败时回退至交易所API。"""
|
||||
start_ms = _parse_time_input(start_time)
|
||||
end_ms = _parse_time_input(end_time)
|
||||
|
||||
refresh_data_service_metadata()
|
||||
if state.DATA_SERVICE_AVAILABLE:
|
||||
try:
|
||||
df = _fetch_kl_from_datasvc(
|
||||
symbol=symbol,
|
||||
timeframe=timeframe,
|
||||
start_ms=start_ms,
|
||||
end_ms=end_ms,
|
||||
limit=limit,
|
||||
)
|
||||
if df is not None and not df.empty:
|
||||
return df
|
||||
except Exception as exc:
|
||||
logger.warning("数据服务请求失败，准备回退至交易所 API：%s", exc)
|
||||
|
||||
return _get_crypto_kl_data_via_ccxt(symbol, timeframe, limit, start_time, end_time)
|
||||
|
||||
|
||||
def get_a_stock_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
|
||||
"""获取A股K线数据"""
|
||||
try:
|
||||
# 处理时间戳参数转换为日期字符串
|
||||
start_date = None
|
||||
end_date = None
|
||||
|
||||
if start_time:
|
||||
try:
|
||||
# 尝试解析时间戳（毫秒）
|
||||
start_timestamp = int(start_time)
|
||||
start_date = datetime.fromtimestamp(start_timestamp / 1000).strftime('%Y-%m-%d')
|
||||
except (ValueError, TypeError):
|
||||
# 如果不是时间戳，尝试解析datetime-local格式 (YYYY-MM-DDTHH:MM)
|
||||
try:
|
||||
if 'T' in str(start_time):
|
||||
# datetime-local格式：2025-05-19T06:07
|
||||
start_date = str(start_time).split('T')[0] # 只取日期部分
|
||||
else:
|
||||
start_date = str(start_time)
|
||||
except:
|
||||
start_date = start_time
|
||||
|
||||
if end_time:
|
||||
try:
|
||||
# 尝试解析时间戳（毫秒）
|
||||
end_timestamp = int(end_time)
|
||||
end_date = datetime.fromtimestamp(end_timestamp / 1000).strftime('%Y-%m-%d')
|
||||
except (ValueError, TypeError):
|
||||
# 如果不是时间戳，尝试解析datetime-local格式
|
||||
try:
|
||||
if 'T' in str(end_time):
|
||||
# datetime-local格式：2025-05-26T06:07
|
||||
end_date = str(end_time).split('T')[0] # 只取日期部分
|
||||
else:
|
||||
end_date = str(end_time)
|
||||
except:
|
||||
end_date = end_time
|
||||
|
||||
# 如果用户指定了时间范围，优先获取该范围内的所有数据
|
||||
actual_limit = limit
|
||||
if start_date and end_date:
|
||||
actual_limit = None # 不限制数据条数，获取完整时间范围数据
|
||||
|
||||
# 调用A股数据获取器
|
||||
df = state.china_stock.get_kl_data(symbol, timeframe, start_date, end_date, actual_limit)
|
||||
|
||||
if df is None:
|
||||
return None
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
def load_crypto_symbols(limit=200):
|
||||
"""加载常见USDT永续合约交易对，返回列表"""
|
||||
refresh_data_service_metadata()
|
||||
if state.SYMBOLS:
|
||||
return state.SYMBOLS[:limit]
|
||||
try:
|
||||
markets = state.exchange.load_markets()
|
||||
symbols = [s for s in markets.keys() if '/USDT' in s and ':USDT' in s]
|
||||
return symbols[:limit]
|
||||
except Exception:
|
||||
return DEFAULT_SYMBOLS[:limit]
|
||||
|
||||
@@ -0,0 +1,300 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from chanlun.core.ChanEnum import Chan_BI_DIR, Chan_SEG_DIR, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR
|
||||
|
||||
# 辅助函数，转换缠论方向枚举为整数
|
||||
def convert_direction(direction):
|
||||
"""转换方向枚举为数字"""
|
||||
if direction == Chan_BI_DIR.UP or direction == Chan_SEG_DIR.UP:
|
||||
return 1
|
||||
elif direction == Chan_BI_DIR.DOWN or direction == Chan_SEG_DIR.DOWN:
|
||||
return -1
|
||||
else:
|
||||
return 0
|
||||
|
||||
def format_time_safely(time_obj, client_tz):
|
||||
"""安全地格式化时间对象，处理字符串和datetime两种情况"""
|
||||
if time_obj is None:
|
||||
return None
|
||||
|
||||
if isinstance(time_obj, str):
|
||||
# 尝试将字符串解析为datetime
|
||||
try:
|
||||
from dateutil import parser
|
||||
time_obj = parser.parse(time_obj)
|
||||
return time_obj.astimezone(client_tz).isoformat()
|
||||
except:
|
||||
return time_obj
|
||||
else:
|
||||
# 已经是datetime对象
|
||||
return time_obj.astimezone(client_tz).isoformat()
|
||||
|
||||
def serialize_chan_macd_data(chan_macd_data, client_tz):
|
||||
"""序列化ChanMACD数据为JSON可序列化格式"""
|
||||
serialized_data = {
|
||||
'seg_list': [],
|
||||
'unittf_list': [],
|
||||
'histset_list': [],
|
||||
# 状态标记数据
|
||||
'high_position_list': [],
|
||||
'high_empty_list': [],
|
||||
'low_position_list': [],
|
||||
'low_empty_list': [],
|
||||
'return_zero_list': [],
|
||||
'cross0_up_list': [],
|
||||
'cross0_down_list': [],
|
||||
# 新增：输出KLU的继续背驰/分离背驰标志
|
||||
'klu_list': []
|
||||
}
|
||||
|
||||
# 序列化seg_list
|
||||
for seg in chan_macd_data.get('seg_list', []):
|
||||
try:
|
||||
seg_data = {
|
||||
'start_time': format_time_safely(seg.start_time, client_tz),
|
||||
'end_time': format_time_safely(seg.end_time, client_tz) if seg.end_time else None,
|
||||
'seg_dir': 'ABOVE' if seg.seg_dir == Chan_MACDSEG_DIR.ABOVE else 'UNDER',
|
||||
'klu_count': len(seg.klu_list) if hasattr(seg, 'klu_list') else 0,
|
||||
'unittf_count': len(seg.unittf_list) if hasattr(seg, 'unittf_list') else 0,
|
||||
'histset_count': len(seg.hist_set) if hasattr(seg, 'hist_set') else 0
|
||||
}
|
||||
serialized_data['seg_list'].append(seg_data)
|
||||
except Exception as e:
|
||||
print(f"序列化seg出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化unittf_list（兼容新结构与枚举类型）
|
||||
for unittf in chan_macd_data.get('unittf_list', []):
|
||||
try:
|
||||
dir_value = getattr(unittf, 'uinttf_dir', None)
|
||||
dir_name = getattr(dir_value, 'name', dir_value if isinstance(dir_value, str) else None)
|
||||
start_t = getattr(unittf, 'start_type', None)
|
||||
start_type = getattr(start_t, 'name', start_t)
|
||||
end_t = getattr(unittf, 'end_type', None)
|
||||
end_type = getattr(end_t, 'name', end_t)
|
||||
peak_abs = getattr(unittf, 'peak_abs', None)
|
||||
if peak_abs is None:
|
||||
peak_abs = getattr(unittf, 'peak_hist', None)
|
||||
length = getattr(unittf, 'length', None)
|
||||
if length is None:
|
||||
length = len(unittf.klu_list) if hasattr(unittf, 'klu_list') else None
|
||||
|
||||
unittf_data = {
|
||||
'start_time': format_time_safely(getattr(unittf, 'start_time', None), client_tz),
|
||||
'end_time': format_time_safely(getattr(unittf, 'end_time', None), client_tz) if getattr(unittf, 'end_time', None) else None,
|
||||
'dir': dir_name, # 'ABOVE' | 'UNDER' | None
|
||||
'start_type': start_type, # e.g. 'START' | 'CROSS0' | 'NEAR0_UP' | 'NEAR0_DOWN'
|
||||
'end_type': end_type,
|
||||
'invalid': getattr(unittf, 'invalid', False),
|
||||
'peak_abs': peak_abs,
|
||||
'length': length,
|
||||
'klu_count': len(unittf.klu_list) if hasattr(unittf, 'klu_list') else 0,
|
||||
'histset_count': len(unittf.histset_list) if hasattr(unittf, 'histset_list') else 0
|
||||
}
|
||||
serialized_data['unittf_list'].append(unittf_data)
|
||||
except Exception as e:
|
||||
print(f"序列化unittf出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化histset_list
|
||||
for histset in chan_macd_data.get('histset_list', []):
|
||||
try:
|
||||
histset_data = {
|
||||
'start_time': format_time_safely(getattr(histset, 'start_time', None), client_tz),
|
||||
'end_time': format_time_safely(getattr(histset, 'end_time', None), client_tz),
|
||||
'histset_dir': 'ABOVE' if histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE else 'UNDER',
|
||||
'klu_count': len(histset.klu_list) if hasattr(histset, 'klu_list') else 0
|
||||
}
|
||||
serialized_data['histset_list'].append(histset_data)
|
||||
except Exception as e:
|
||||
print(f"序列化histset出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化状态标记数据
|
||||
# 序列化高位列表
|
||||
for high_pos in chan_macd_data.get('high_position_list', []):
|
||||
try:
|
||||
high_pos_data = {
|
||||
'time': format_time_safely(high_pos['time'], client_tz),
|
||||
'end_time': format_time_safely(high_pos.get('end_time'), client_tz) if high_pos.get('end_time') else None,
|
||||
'type': high_pos.get('type', 'start'),
|
||||
'macd': high_pos.get('macd'),
|
||||
'signal': high_pos.get('signal'),
|
||||
'macdhist': high_pos.get('macdhist'),
|
||||
'end_macd': high_pos.get('end_macd'),
|
||||
'end_signal': high_pos.get('end_signal'),
|
||||
'end_macdhist': high_pos.get('end_macdhist')
|
||||
}
|
||||
serialized_data['high_position_list'].append(high_pos_data)
|
||||
except Exception as e:
|
||||
print(f"序列化high_position出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化高位空列表
|
||||
for high_empty in chan_macd_data.get('high_empty_list', []):
|
||||
try:
|
||||
high_empty_data = {
|
||||
'time': format_time_safely(high_empty['time'], client_tz),
|
||||
'end_time': format_time_safely(high_empty.get('end_time'), client_tz) if high_empty.get('end_time') else None,
|
||||
'type': high_empty.get('type', 'start'),
|
||||
'macd': high_empty.get('macd'),
|
||||
'signal': high_empty.get('signal'),
|
||||
'macdhist': high_empty.get('macdhist'),
|
||||
'end_macd': high_empty.get('end_macd'),
|
||||
'end_signal': high_empty.get('end_signal'),
|
||||
'end_macdhist': high_empty.get('end_macdhist')
|
||||
}
|
||||
serialized_data['high_empty_list'].append(high_empty_data)
|
||||
except Exception as e:
|
||||
print(f"序列化high_empty出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化低位与低位空
|
||||
for low_pos in chan_macd_data.get('low_position_list', []):
|
||||
try:
|
||||
low_pos_data = {
|
||||
'time': format_time_safely(low_pos['time'], client_tz),
|
||||
'end_time': format_time_safely(low_pos.get('end_time'), client_tz) if low_pos.get('end_time') else None,
|
||||
'type': low_pos.get('type', 'start'),
|
||||
'macd': low_pos.get('macd'),
|
||||
'signal': low_pos.get('signal'),
|
||||
'macdhist': low_pos.get('macdhist'),
|
||||
'end_macd': low_pos.get('end_macd'),
|
||||
'end_signal': low_pos.get('end_signal'),
|
||||
'end_macdhist': low_pos.get('end_macdhist')
|
||||
}
|
||||
serialized_data['low_position_list'].append(low_pos_data)
|
||||
except Exception as e:
|
||||
print(f"序列化low_position出错: {e}")
|
||||
continue
|
||||
|
||||
for low_empty in chan_macd_data.get('low_empty_list', []):
|
||||
try:
|
||||
low_empty_data = {
|
||||
'time': format_time_safely(low_empty['time'], client_tz),
|
||||
'end_time': format_time_safely(low_empty.get('end_time'), client_tz) if low_empty.get('end_time') else None,
|
||||
'type': low_empty.get('type', 'start'),
|
||||
'macd': low_empty.get('macd'),
|
||||
'signal': low_empty.get('signal'),
|
||||
'macdhist': low_empty.get('macdhist'),
|
||||
'end_macd': low_empty.get('end_macd'),
|
||||
'end_signal': low_empty.get('end_signal'),
|
||||
'end_macdhist': low_empty.get('end_macdhist')
|
||||
}
|
||||
serialized_data['low_empty_list'].append(low_empty_data)
|
||||
except Exception as e:
|
||||
print(f"序列化low_empty出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化归零轴列表
|
||||
for return_zero in chan_macd_data.get('return_zero_list', []):
|
||||
try:
|
||||
return_zero_data = {
|
||||
'time': format_time_safely(return_zero['time'], client_tz),
|
||||
'end_time': format_time_safely(return_zero.get('end_time'), client_tz) if return_zero.get('end_time') else None,
|
||||
'type': return_zero.get('type', 'start'),
|
||||
'macd': return_zero.get('macd'),
|
||||
'signal': return_zero.get('signal'),
|
||||
'macdhist': return_zero.get('macdhist'),
|
||||
'end_macd': return_zero.get('end_macd'),
|
||||
'end_signal': return_zero.get('end_signal'),
|
||||
'end_macdhist': return_zero.get('end_macdhist')
|
||||
}
|
||||
serialized_data['return_zero_list'].append(return_zero_data)
|
||||
except Exception as e:
|
||||
print(f"序列化return_zero出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化穿越零轴列表
|
||||
for cross0_up in chan_macd_data.get('cross0_up_list', []):
|
||||
try:
|
||||
cross0_up_data = {
|
||||
'time': format_time_safely(cross0_up['time'], client_tz),
|
||||
'type': cross0_up.get('type', 'start'),
|
||||
'macd': cross0_up.get('macd'),
|
||||
'signal': cross0_up.get('signal'),
|
||||
'macdhist': cross0_up.get('macdhist')
|
||||
}
|
||||
serialized_data['cross0_up_list'].append(cross0_up_data)
|
||||
except Exception as e:
|
||||
print(f"序列化cross0_up出错: {e}")
|
||||
continue
|
||||
|
||||
for cross0_down in chan_macd_data.get('cross0_down_list', []):
|
||||
try:
|
||||
cross0_down_data = {
|
||||
'time': format_time_safely(cross0_down['time'], client_tz),
|
||||
'type': cross0_down.get('type', 'start'),
|
||||
'macd': cross0_down.get('macd'),
|
||||
'signal': cross0_down.get('signal'),
|
||||
'macdhist': cross0_down.get('macdhist')
|
||||
}
|
||||
serialized_data['cross0_down_list'].append(cross0_down_data)
|
||||
except Exception as e:
|
||||
print(f"序列化cross0_down出错: {e}")
|
||||
continue
|
||||
|
||||
# 序列化 KLU 列表（仅导出需要的时间与背驰标志）
|
||||
for klu in chan_macd_data.get('klu_list', []):
|
||||
try:
|
||||
serialized_data['klu_list'].append({
|
||||
'time': format_time_safely(getattr(klu, 'time', None), client_tz),
|
||||
'continue_div': bool(getattr(klu, 'continue_div', False)),
|
||||
'separate_div': int(getattr(klu, 'separate_div', 0)) if getattr(klu, 'separate_div', 0) is not None else 0,
|
||||
'near0_return': int(getattr(klu, 'near0_return', 0)) if getattr(klu, 'near0_return', 0) is not None else 0
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"序列化klu出错: {e}")
|
||||
continue
|
||||
|
||||
return serialized_data
|
||||
|
||||
def clean_dataframe_for_json(df):
|
||||
"""清理DataFrame数据用于JSON序列化"""
|
||||
# 创建副本避免修改原始数据
|
||||
clean_df = df.copy()
|
||||
|
||||
# 替换NaN值为None
|
||||
clean_df = clean_df.where(pd.notnull(clean_df), None)
|
||||
|
||||
return clean_df
|
||||
|
||||
def get_uncompleted_seg_list(seg_list, client_tz):
|
||||
"""获取未完成线段列表，正确处理倒数第二个和最后一个未完成线段"""
|
||||
uncompleted_segs = [seg for seg in seg_list if not seg.is_sure]
|
||||
|
||||
if len(uncompleted_segs) == 0:
|
||||
return []
|
||||
|
||||
result = []
|
||||
|
||||
for i, seg in enumerate(uncompleted_segs):
|
||||
is_last = (i == len(uncompleted_segs) - 1) # 是否为最后一个未完成线段
|
||||
|
||||
seg_data = {
|
||||
'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(),
|
||||
'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,
|
||||
'direction': convert_direction(seg.dir)
|
||||
}
|
||||
|
||||
if is_last:
|
||||
# 最后一个未完成线段：没有结束时间和价格
|
||||
seg_data['end_time'] = None
|
||||
seg_data['end_price'] = None
|
||||
else:
|
||||
# 倒数第二个及之前的未完成线段：使用实际的结束时间和价格
|
||||
if seg.end_bi and seg.end_bi.end_klc:
|
||||
seg_data['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()
|
||||
seg_data['end_price'] = seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low
|
||||
else:
|
||||
# 如果没有结束笔，设为None
|
||||
seg_data['end_time'] = None
|
||||
seg_data['end_price'] = None
|
||||
|
||||
result.append(seg_data)
|
||||
|
||||
return result
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import os
|
||||
from collections import OrderedDict
|
||||
import logging
|
||||
|
||||
import ccxt
|
||||
|
||||
_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
if _ROOT not in sys.path:
|
||||
sys.path.append(_ROOT)
|
||||
|
||||
from config import MACD_FAST, MACD_SLOW, MACD_SIGNAL, ccxt_proxies
|
||||
from services.cn_stock import ChinaStockData
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class TRADE_POINT_TYPE:
|
||||
BUY1 = 1 # 一类买点
|
||||
BUY2 = 2 # 二类买点
|
||||
BUY3 = 3 # 三类买点
|
||||
SELL1 = -1 # 一类卖点
|
||||
SELL2 = -2 # 二类卖点
|
||||
SELL3 = -3 # 三类卖点
|
||||
|
||||
|
||||
|
||||
# mutable runtime state
|
||||
macd_fast_period = MACD_FAST
|
||||
macd_slow_period = MACD_SLOW
|
||||
macd_signal_period = MACD_SIGNAL
|
||||
|
||||
_proxies = ccxt_proxies()
|
||||
_exchange_kwargs = {"enableRateLimit": True}
|
||||
if _proxies:
|
||||
_exchange_kwargs["proxies"] = _proxies
|
||||
exchange = ccxt.binance(_exchange_kwargs)
|
||||
|
||||
china_stock = ChinaStockData()
|
||||
_zone_cache = {}
|
||||
|
||||
DEFAULT_TIMEFRAME_LABELS = OrderedDict([
|
||||
("1m", "1分钟"),
|
||||
("3m", "3分钟"),
|
||||
("5m", "5分钟"),
|
||||
("15m", "15分钟"),
|
||||
("30m", "30分钟"),
|
||||
("1h", "1小时"),
|
||||
("2h", "2小时"),
|
||||
("4h", "4小时"),
|
||||
("6h", "6小时"),
|
||||
("8h", "8小时"),
|
||||
("12h", "12小时"),
|
||||
("1d", "日线"),
|
||||
("3d", "3日线"),
|
||||
("1w", "周线"),
|
||||
("1M", "月线"),
|
||||
])
|
||||
|
||||
DEFAULT_SYMBOLS = [
|
||||
'SOL/USDT:USDT', 'BTC/USDT:USDT', 'ETH/USDT:USDT', 'BNB/USDT:USDT', 'XRP/USDT:USDT', 'WIF/USDT:USDT',
|
||||
'ADA/USDT:USDT', 'DOGE/USDT:USDT', 'AVAX/USDT:USDT', 'DOT/USDT:USDT', 'MATIC/USDT:USDT'
|
||||
]
|
||||
|
||||
TIMEFRAMES = DEFAULT_TIMEFRAME_LABELS.copy()
|
||||
SYMBOLS = DEFAULT_SYMBOLS.copy()
|
||||
DATA_SERVICE_AVAILABLE = False
|
||||
SERVICE_METADATA_LAST_REFRESH = 0
|
||||
|
||||
@@ -0,0 +1,121 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import OrderedDict
|
||||
from .state import DEFAULT_TIMEFRAME_LABELS
|
||||
|
||||
def _zone_cache_ttl(tf_name: str) -> int:
|
||||
"""根据时间周期返回缓存过期时间（秒）"""
|
||||
minutes = timeframe_to_minutes(tf_name) or 5
|
||||
if minutes <= 5:
|
||||
return 120 # 5m及以下: 2分钟
|
||||
elif minutes <= 15:
|
||||
return 300 # 15m: 5分钟
|
||||
elif minutes <= 60:
|
||||
return 600 # 1h: 10分钟
|
||||
else:
|
||||
return 1800 # 4h+: 30分钟
|
||||
|
||||
|
||||
def timeframe_to_minutes(tf: str):
|
||||
"""将时间周期转换为分钟数，用于排序。"""
|
||||
if not tf:
|
||||
return None
|
||||
unit = tf[-1]
|
||||
try:
|
||||
value = int(tf[:-1])
|
||||
except (ValueError, TypeError):
|
||||
return None
|
||||
multiplier = {
|
||||
'm': 1,
|
||||
'h': 60,
|
||||
'd': 1440,
|
||||
'w': 10080,
|
||||
'M': 43200, # 30天近似
|
||||
}.get(unit)
|
||||
if multiplier is None:
|
||||
return None
|
||||
return value * multiplier
|
||||
|
||||
|
||||
def format_timeframe_label(tf: str) -> str:
|
||||
"""将时间周期转换为可读标签。"""
|
||||
if not tf:
|
||||
return tf
|
||||
unit = tf[-1]
|
||||
try:
|
||||
value = int(tf[:-1])
|
||||
except (ValueError, TypeError):
|
||||
return tf
|
||||
if unit == 'm':
|
||||
return f"{value}分钟"
|
||||
if unit == 'h':
|
||||
return f"{value}小时"
|
||||
if unit == 'd':
|
||||
return "日线" if value == 1 else f"{value}日线"
|
||||
if unit == 'w':
|
||||
return "周线" if value == 1 else f"{value}周线"
|
||||
if unit == 'M':
|
||||
return "月线" if value == 1 else f"{value}月线"
|
||||
return tf
|
||||
|
||||
|
||||
def build_timeframe_labels(timeframes):
|
||||
ordered = sorted(
|
||||
timeframes,
|
||||
key=lambda tf: timeframe_to_minutes(tf) if timeframe_to_minutes(tf) is not None else float('inf'),
|
||||
)
|
||||
labels = OrderedDict()
|
||||
for tf in ordered:
|
||||
labels[tf] = format_timeframe_label(tf)
|
||||
return labels
|
||||
|
||||
|
||||
def compute_timeframe_defaults(labels_ordered):
|
||||
"""
|
||||
根据已排序的「周期 → 中文标签」映射，计算主 / 次 / 次次周期默认值。
|
||||
labels_ordered: OrderedDict 或按插入顺序排列的 dict。
|
||||
"""
|
||||
if not labels_ordered:
|
||||
labels_ordered = DEFAULT_TIMEFRAME_LABELS.copy()
|
||||
timeframe_keys = list(labels_ordered.keys())
|
||||
preferred_main = next((tf for tf in ['5m', '15m', '1h'] if tf in labels_ordered), None)
|
||||
default_main = preferred_main or (timeframe_keys[0] if timeframe_keys else '1m')
|
||||
if default_main not in labels_ordered and timeframe_keys:
|
||||
default_main = timeframe_keys[0]
|
||||
|
||||
if timeframe_keys:
|
||||
try:
|
||||
idx = timeframe_keys.index(default_main)
|
||||
default_element = timeframe_keys[idx - 1] if idx > 0 else timeframe_keys[0]
|
||||
except ValueError:
|
||||
default_element = timeframe_keys[0]
|
||||
else:
|
||||
default_element = default_main
|
||||
|
||||
if timeframe_keys:
|
||||
try:
|
||||
idx_el = timeframe_keys.index(default_element)
|
||||
default_sub_sub = timeframe_keys[idx_el - 1] if idx_el > 0 else timeframe_keys[0]
|
||||
except ValueError:
|
||||
default_sub_sub = timeframe_keys[0]
|
||||
else:
|
||||
default_sub_sub = default_element
|
||||
|
||||
return default_main, default_element, default_sub_sub, timeframe_keys
|
||||
|
||||
def is_smaller_timeframe(tf1, tf2):
|
||||
"""判断时间周期tf1是否小于tf2"""
|
||||
tf1_value = timeframe_to_minutes(tf1)
|
||||
tf2_value = timeframe_to_minutes(tf2)
|
||||
if tf1_value is None or tf2_value is None:
|
||||
return False
|
||||
return tf1_value < tf2_value
|
||||
|
||||
def is_smaller_or_equal_timeframe(tf1, tf2):
|
||||
"""判断时间周期tf1是否小于等于tf2"""
|
||||
tf1_value = timeframe_to_minutes(tf1)
|
||||
tf2_value = timeframe_to_minutes(tf2)
|
||||
if tf1_value is None or tf2_value is None:
|
||||
return False
|
||||
return tf1_value <= tf2_value
|
||||
|
||||
@@ -1,14 +1,53 @@
|
||||
""" /api/analyze 契约冒烟：关键字段存在于契约清单。"""
|
||||
"""ECR-002：加深 /api/analyze 相关契约 —— mock 行情 + analyze_chan 关键字段快照。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from unittest.mock import patch
|
||||
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
sys.path.insert(0, str(ROOT))
|
||||
sys.path.insert(0, str(ROOT / "web"))
|
||||
|
||||
from tests.generate_golden import make_ohlcv # noqa: E402
|
||||
|
||||
|
||||
CONTRACT_KEYS = json.loads(
|
||||
(ROOT / "tests" / "fixtures" / "analyze_contract_keys.json").read_text(encoding="utf-8")
|
||||
)
|
||||
|
||||
# analyze_chan 直接返回的对象字段（未序列化前）
|
||||
ANALYZE_CHAN_KEYS = {
|
||||
"klc_list",
|
||||
"klu_list",
|
||||
"bi_list",
|
||||
"seg_list",
|
||||
"zs_list",
|
||||
"bi_zs_list",
|
||||
"bsp_list",
|
||||
"klc_fx_info",
|
||||
"chan_macd",
|
||||
"ema52_dict",
|
||||
}
|
||||
|
||||
CHAN_MACD_SERIALIZED_KEYS = {
|
||||
"seg_list",
|
||||
"unittf_list",
|
||||
"histset_list",
|
||||
"high_position_list",
|
||||
"high_empty_list",
|
||||
"low_position_list",
|
||||
"low_empty_list",
|
||||
"return_zero_list",
|
||||
"cross0_up_list",
|
||||
"cross0_down_list",
|
||||
"klu_list",
|
||||
}
|
||||
|
||||
|
||||
def test_analyze_route_registered():
|
||||
from app import app
|
||||
@@ -21,9 +60,60 @@ def test_analyze_route_registered():
|
||||
|
||||
|
||||
def test_contract_keys_stable():
|
||||
keys = json.loads(
|
||||
(ROOT / "tests" / "fixtures" / "analyze_contract_keys.json").read_text(
|
||||
encoding="utf-8"
|
||||
assert "bi_list" in CONTRACT_KEYS and "seg_list" in CONTRACT_KEYS
|
||||
for k in ("kline_data", "macd", "zs_list", "bsp_list", "chan_macd"):
|
||||
assert k in CONTRACT_KEYS
|
||||
|
||||
|
||||
def test_analyze_chan_keys_on_fixture():
|
||||
from services.runtime import add_indicators, analyze_chan
|
||||
|
||||
df = add_indicators(make_ohlcv(400))
|
||||
result = analyze_chan(df, symbol="TEST/USDT:USDT", timeframe="5m")
|
||||
assert set(result.keys()) == ANALYZE_CHAN_KEYS
|
||||
assert isinstance(result["bi_list"], list)
|
||||
assert isinstance(result["seg_list"], list)
|
||||
assert isinstance(result["chan_macd"], dict)
|
||||
for k in ("seg_list", "unittf_list", "histset_list"):
|
||||
assert k in result["chan_macd"]
|
||||
|
||||
|
||||
def test_serialize_chan_macd_shape():
|
||||
from pytz import timezone
|
||||
|
||||
from services.runtime import add_indicators, analyze_chan, serialize_chan_macd_data
|
||||
|
||||
df = add_indicators(make_ohlcv(200))
|
||||
result = analyze_chan(df)
|
||||
serialized = serialize_chan_macd_data(result["chan_macd"], timezone("Asia/Shanghai"))
|
||||
assert set(serialized.keys()) == CHAN_MACD_SERIALIZED_KEYS
|
||||
# JSON 可序列化
|
||||
json.dumps(serialized)
|
||||
|
||||
|
||||
def test_analyze_http_contract_with_mocked_kl():
|
||||
"""Flask 测试客户端：mock get_kl_data，断言响应含契约关键字段。"""
|
||||
from app import app
|
||||
from services.runtime import add_indicators
|
||||
|
||||
df = add_indicators(make_ohlcv(300))
|
||||
df = df.copy()
|
||||
if "timestamp" not in df.columns:
|
||||
df["timestamp"] = (pd.to_datetime(df["date"]).astype("int64") // 10**6).astype("int64")
|
||||
|
||||
# analyze 路由使用 `from services.runtime import *`，须 patch 其模块命名空间
|
||||
with patch("api.analyze.get_kl_data", return_value=df):
|
||||
client = app.test_client()
|
||||
resp = client.get(
|
||||
"/api/analyze",
|
||||
query_string={
|
||||
"symbol": "BTC/USDT:USDT",
|
||||
"timeframe": "5m",
|
||||
"timezone": "Asia/Shanghai",
|
||||
},
|
||||
)
|
||||
)
|
||||
assert "bi_list" in keys and "seg_list" in keys
|
||||
assert resp.status_code == 200, resp.data[:500]
|
||||
payload = resp.get_json()
|
||||
assert payload is not None and "error" not in payload
|
||||
missing = [k for k in CONTRACT_KEYS if k not in payload]
|
||||
assert not missing, f"missing contract keys: {missing}"
|
||||
|
||||
@@ -0,0 +1,55 @@
|
||||
"""ECR-002：runtime 门面公开符号 + 子模块可导入。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
sys.path.insert(0, str(ROOT))
|
||||
sys.path.insert(0, str(ROOT / "web"))
|
||||
|
||||
REQUIRED = [
|
||||
"get_kl_data",
|
||||
"analyze_chan",
|
||||
"add_indicators",
|
||||
"serialize_chan_macd_data",
|
||||
"clean_dataframe_for_json",
|
||||
"classify_trend_stage",
|
||||
"refresh_data_service_metadata",
|
||||
"TIMEFRAMES",
|
||||
"SYMBOLS",
|
||||
"_zone_cache",
|
||||
"macd_fast_period",
|
||||
"is_smaller_or_equal_timeframe",
|
||||
"get_uncompleted_seg_list",
|
||||
]
|
||||
|
||||
|
||||
def test_runtime_facade_exports():
|
||||
from services import runtime as R
|
||||
|
||||
for name in REQUIRED:
|
||||
assert hasattr(R, name), f"missing facade export: {name}"
|
||||
|
||||
|
||||
def test_runtime_submodules_importable():
|
||||
from services.runtime import state, timeframes, market_data, indicators, analyze, serialize
|
||||
|
||||
assert state.exchange is not None
|
||||
assert callable(timeframes.timeframe_to_minutes)
|
||||
assert callable(market_data.get_kl_data)
|
||||
assert callable(indicators.add_indicators)
|
||||
assert callable(analyze.analyze_chan)
|
||||
assert callable(serialize.convert_direction)
|
||||
|
||||
|
||||
def test_thin_shims_still_reexport():
|
||||
from services import market_data as md
|
||||
from services import chan_analyze as ca
|
||||
from services import serializers as ser
|
||||
from services import timeframes as tf
|
||||
|
||||
assert callable(md.get_kl_data)
|
||||
assert callable(ca.analyze_chan)
|
||||
assert callable(ser.serialize_chan_macd_data)
|
||||
assert callable(tf.timeframe_to_minutes)
|
||||
Reference in New Issue
Block a user