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添加ema52显示

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This commit is contained in:
jackyu66git
2025-09-27 01:14:53 +08:00
parent 68e9c38922
commit 6139af98c3
5 changed files with 389 additions and 88 deletions
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ChanLun.py
+60 -27
View File
@@ -21,29 +21,61 @@ from ChanMACD import ChanMACD
from TF_DF import TF_DF
class ChanLun():
time1 = 1
time3 = 3
time5 = 5
time10 = 10
time15 = 15
time30 = 30
time60 = 60
time2h = 120
time4h = 240
time6h = 360
time8h = 480
time12h = 720
time1d = 1440
timeframes = [time1, time3, time5, time10, time15, time30, time60]
tf_df_dict = {}
def init_data(self, dataframe, ticker_indicator):
for timeframe in self.timeframes:
self.tf_df_dict[timeframe] = TF_DF(timeframe, dataframe, ticker_indicator)
def cal_bsp(self, dataframe, ticker_indicator):
# 初始化多周期数据
self.init_data(dataframe, ticker_indicator)
def __init__(self):
self.time3m = 3
self.time5m = 5
self.time10m = 10
self.time15m = 15
self.time30m = 30
self.time_m_intervals = [3, 5, 10, 15, 30]
self.time_m_symbols = ['3m', '5m', '10m', '15m', '30m']
self.time2h = 2*60
self.time4h = 4*60
self.time6h = 6*60
self.time8h = 8*60
self.time12h = 12*60
self.time16h = 16*60
self.time_h_intervals = [2*60, 4*60, 6*60, 8*60, 12*60, 16*60]
self.time_h_symbols = ['2h', '4h', '6h', '8h', '12h', '16h']
self.time2d = 2*24*60
self.time3d = 3*24*60
self.time1w = 7*24*60
self.time2w = 14*24*60
self.time_d_intervals = [2*24*60, 3*24*60, 7*24*60, 14*24*60]
self.time_d_symbols = ['2d', '3d', '1w', '2w']
self.time2M = 2*30*24*60
self.time3M = 3*30*24*60
self.time6M = 6*30*24*60
self.time1y = 12*30*24*60
self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
self.time_M_symbols = ['2M', '3M', '6M', '1y']
self.time_symbols = ['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d', '1w', '2w', '1M', '3M', '6M', '1y']
self.tf_df_dict = {}
self.ema_symbols = ['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d', '1w', '2w']
def init_data(self, dataframe, intervals, timeframes):
for index in range(0, len(intervals)):
timeframe = timeframes[index]
interval = intervals[index]
self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
def init_dataframes(self, dataframe_m, dataframe_h, dataframe_d, dataframe_M):
self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
def get_ema52_dict(self):
if len(self.tf_df_dict) > 0:
return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
return None
def get_ema24_dict(self):
if len(self.tf_df_dict) > 0:
return {key: self.tf_df_dict[key].get_ema24() for key in self.ema_symbols}
return None
def cal_bsp(self):
return
def check_fx(self, klc):
if klc.pre and klc.next:
if klc.high > klc.pre.high and klc.high > klc.next.high:
@@ -444,13 +476,14 @@ class ChanLun():
hist = getattr(klc, 'macdhist', 0) if getattr(klc, 'macdhist', None) is not None else 0
rsi = getattr(klc, 'rsi', None)
trend = Chan_PRICE_TREND.UNKNOWN
score = 0
try:
# 有效性
price_valid = price is not None and price != 0
ema24_valid = ema24 is not None and ema24 != 0
ema52_valid = ema52 is not None and ema52 != 0
# 多因子投票
score = 0
# 1) 均线结构 + 价位
if ema24_valid and ema52_valid:
score += 1 if ema24 > ema52 else -1
@@ -542,9 +575,9 @@ class ChanLun():
setattr(klc, 'trend', trend)
last_trend = trend
price_diff = klc.close - klc.pre.close if klc.pre else 0
if klc.index > len(klc_list) - 10:
print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff)
#print(klc.start_time, klc.end_time, klc.trend, price_diff)
#if klc.index > len(klc_list) - 10:
#print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff, score)
#print(klc.start_time, klc.end_time, klc.trend, price_diff, score)
return klc_list
def cal_bi_list(self, klc_list):
bi_list = []
TF_DF.py
+20 -6
View File
@@ -20,11 +20,11 @@ import numpy as np
from ChanMACD import ChanMACD
class TF_DF():
def __init__(self, timeframe, df, ticker_indicator):
def __init__(self, df, interval, timeframe):
self.timeframe = timeframe
self.dataframe = resample_to_interval(df, ticker_indicator*timeframe)
self.interval = interval
self.dataframe = resample_to_interval(df, interval)
self.dataframe = self.add_indicators(self.dataframe)
self.ticker_indicator = ticker_indicator
self.klu_list = []
self.klc_list = []
self.bi_list = []
@@ -40,6 +40,22 @@ class TF_DF():
self.zs_list = self.cal_zs_list(self.bi_list, self.seg_list)
self.chanmacd = ChanMACD(self.klu_list)
self.klu_list = self.chanmacd.cal_macd_state()
def get_ema52(self):
if self.klu_list:
ema52_value = self.klu_list[-1].ema52
# 处理NaN值
if pd.isna(ema52_value) or ema52_value is None:
return None
return float(ema52_value)
return None
def get_ema24(self):
if self.klu_list:
ema24_value = self.klu_list[-1].ema24
# 处理NaN值
if pd.isna(ema24_value) or ema24_value is None:
return None
return float(ema24_value)
return None
def add_indicators(self, df):
fast = 12
slow = 26
@@ -223,7 +239,7 @@ class TF_DF():
setattr(klc, 'trend', trend)
last_trend = trend
price_diff = klc.close - klc.pre.close if klc.pre else 0
print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff)
#print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff)
#print(klc.start_time, klc.end_time, klc.trend, price_diff)
return klc_list
def cal_kl_data(self, dataframe:DataFrame):
@@ -627,7 +643,6 @@ class TF_DF():
bi_list[-1].add_klc(klc)
klc.set_bi(bi_list[-1])
#klc.set_klc_fx_type(Chan_KLC_FX.TOP3)
klc.set_last_top_klu(last_top)
#print(klc.start_time, klc.fx, "二类卖点Sell 1")
else:
# A new top found
@@ -752,7 +767,6 @@ class TF_DF():
bi_list[-1].add_klc(klc)
klc.set_bi(bi_list[-1])
#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM3)
klc.set_last_bottom_klc(last_bottom)
#print(last_bottom.start_time, last_bottom.end_time, "--------------------------------1")
#print(klc.start_time, klc.fx, "二类买点Buy 1")
else:
strategies/ChanLun_BTC.py
+63 -44
View File
@@ -23,6 +23,7 @@ logger = logging.getLogger(__name__)
# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange=20250901-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
@@ -76,36 +77,56 @@ class ChanLun_BTC(IStrategy):
# 关闭分批止盈/仓位调整
position_adjustment_enable = False
startup_candle_count = 2880
time3 = 3
time5 = 5
time15 = 15
time30 = 30
time60 = 60
time2h = 120
time4h = 240
time1d = 1440
startup_candle_count = 1600
time3m = 3
time5m = 5
time10m = 10
time15m = 15
time30m = 30
time_m = [3, 5, 10, 15, 30]
time1h = 60
time2h = 2
time4h = 4
time6h = 6
time8h = 8
time12h = 12
time16h = 16
time_h = [2, 4, 6, 8, 12, 16]
time2d = 2
time3d = 3
time1w = 7
time_d = [2, 3, 7]
time2M = 2
time3M = 3
time_M = [2, 3]
last_time = datetime.now()
chan = ChanLun()
last_order = None
last_trade = None
pair = 'BTC/USDT:USDT'
def informative_pairs(self):
timeframes = ['1h', '1d', '1M']
informative_pairs = [(self.pair, tf) for tf in timeframes]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.add_indicators(dataframe)
# 仅保留15m(用于BSP)与60m(用于ATR过滤/止损)两个重采样
dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
# 计算多周期BSP（以15m为基准），并合并到15m数据上
# 先给重采样帧补指标
dataframe_15 = self.add_indicators(dataframe_15)
dataframe_60 = self.add_indicators(dataframe_60)
# 计算15m BSP
bsp_15 = self.chan.cal_bsp(dataframe, self.get_ticker_indicator())
# 合并15m与60m到主DF，生成 resample_*_* 列
dataframe = resampled_merge(dataframe, dataframe_15)
dataframe = resampled_merge(dataframe, dataframe_60)
self.init_dataframes(dataframe)
return dataframe
def init_dataframes(self, dataframe_m):
dataframe_1h = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
dataframe_1d = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1d')
dataframe_1M = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1M')
self.chan.init_dataframes(dataframe_m, dataframe_1h, dataframe_1d, dataframe_1M)
self.print_all_ema52()
def print_all_ema52(self):
for key, value in self.chan.get_ema52_dict().items():
print(key, value)
def print_all_ema24(self):
for key, value in self.chan.get_ema24_dict().items():
print(key, value)
def add_indicators(self, df):
fast = 12
slow = 26
@@ -212,9 +233,9 @@ class ChanLun_BTC(IStrategy):
return -0.05
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
ema52_str = 'resample_{}_ema52'.format(self.get_ticker_indicator()*self.time15)
ema52_str = 'resample_{}_ema52'.format(self.time15m)
ema52_val = float(last_candle.get(ema52_str, 0) or 0)
close_str = 'resample_{}_close'.format(self.get_ticker_indicator()*self.time15)
close_str = 'resample_{}_close'.format(self.time15m)
close_val = float(last_candle.get(close_str, 0) or 0)
if close_val < ema52_val:
return -0.01
@@ -240,7 +261,7 @@ class ChanLun_BTC(IStrategy):
if dataframe is None or len(dataframe) == 0:
return False
last = dataframe.iloc[-1]
atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator()*self.time60)
atr_str = 'resample_{}_atr'.format(self.time1h)
atr_val = float(last.get(atr_str, 0) or 0)
if atr_val < 0.001:
#logger.info(f"ATR过滤：atr={atr_val:.2f} < 100, 拒绝进场 {pair}")
@@ -263,7 +284,7 @@ class ChanLun_BTC(IStrategy):
# Obtain pair dataframe (just to show how to access it)
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator()*self.time15)
atr_str = 'resample_{}_atr'.format(elf.time15)
# 保存开仓时的ATR值用于止损计算
if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
entry_atr = last_candle[atr_str] * 4
@@ -271,13 +292,13 @@ class ChanLun_BTC(IStrategy):
#logger.info(f"保存开仓时ATR值: {entry_atr}")
return None
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
shift15 = self.time15
shift60 = self.time60
bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
macdh_col = 'resample_{}_macdhist'.format(self.get_ticker_indicator()*shift15)
c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
shift15 = self.time15m
shift60 = self.time1h
bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
score_col = 'resample_{}_mtf_score'.format(shift15)
macdh_col = 'resample_{}_macdhist'.format(shift15)
c60_col = 'resample_{}_close'.format(shift60)
e60_col = 'resample_{}_ema52'.format(shift60)
# 强化过滤：15m BSP + 分数阈值 + 60m 趋势同向 + 15m MACD柱同向
if all(col in dataframe.columns for col in [bsp_col, score_col, macdh_col, c60_col, e60_col]):
dataframe.loc[
@@ -298,12 +319,12 @@ class ChanLun_BTC(IStrategy):
['enter_short', 'enter_tag']] = (1, 'short_bsp15_v2')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
shift15 = self.time15
shift60 = self.time60
bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
shift15 = self.time15m
shift60 = self.time1h
bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
score_col = 'resample_{}_mtf_score'.format(shift15)
c60_col = 'resample_{}_close'.format(shift60)
e60_col = 'resample_{}_ema52'.format(shift60)
# 反向强信号或60m趋势反向时平仓
if all(col in dataframe.columns for col in [bsp_col, score_col, c60_col, e60_col]):
dataframe.loc[
@@ -323,6 +344,4 @@ class ChanLun_BTC(IStrategy):
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return self.lev
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
web/app.py
+45 -9
View File
@@ -346,10 +346,43 @@ def calculate_macd(df):
'histogram': histogram.tolist()
}
def analyze_chan(df):
def analyze_chan(df, symbol=None, timeframe=None):
"""进行缠论分析"""
chan = ChanLun()
# 初始化多时间周期数据以获取EMA52
ema52_dict = None
if symbol and timeframe:
try:
# 获取不同时间周期的数据用于初始化
df_1h = get_kl_data(symbol, '1h', limit=800) if timeframe != '1h' else df
df_1d = get_kl_data(symbol, '1d', limit=800) if timeframe != '1d' else df
df_1M = get_kl_data(symbol, '1M', limit=800) if timeframe != '1M' else df
# 添加指标
if df_1h is not None and len(df_1h) > 0:
df_1h = add_indicators(df_1h)
if df_1d is not None and len(df_1d) > 0:
df_1d = add_indicators(df_1d)
if df_1M is not None and len(df_1M) > 0:
df_1M = add_indicators(df_1M)
# 初始化多时间周期数据
chan.init_dataframes(df, df_1h, df_1d, df_1M)
# 获取EMA52数据
ema52_dict = chan.get_ema52_dict()
# 处理NaN值
if ema52_dict:
for key, value in ema52_dict.items():
if pd.isna(value) or value is None:
ema52_dict[key] = None
else:
ema52_dict[key] = float(value)
except Exception as e:
print(f"获取多时间周期EMA52数据失败: {e}")
ema52_dict = None
# 获取分析结果
klc_list = chan.get_klc_list(df)
bi_list = chan.cal_bi_list(klc_list)
@@ -546,7 +579,8 @@ def analyze_chan(df):
'trade_points': buy_sell_points,
'klc_fx_info': klc_fx_info, # KLC分型信息
'klu_fx_info': klu_fx_info, # 添加KLU分型信息
'chan_macd': chan_macd_data # 添加ChanMACD分析数据
'chan_macd': chan_macd_data, # 添加ChanMACD分析数据
'ema52_dict': ema52_dict # 添加多时间周期EMA52数据
}
def generate_replay_data(df, client_tz, symbol=None, element_timeframe=None, start_time=None, end_time=None):
@@ -572,7 +606,7 @@ def generate_replay_data(df, client_tz, symbol=None, element_timeframe=None, sta
current_df = add_indicators(current_df)
# 进行缠论分析
analysis_result = analyze_chan(current_df)
analysis_result = analyze_chan(current_df, symbol, timeframe)
# 计算MACD
macd_data = calculate_macd(current_df)
@@ -590,7 +624,7 @@ def generate_replay_data(df, client_tz, symbol=None, element_timeframe=None, sta
if len(element_current_df) > 0:
# 重新对当前时间范围的次周期数据进行缠论分析
# 这样可以确保数据的准确性，避免时间筛选的复杂性
element_current_analysis = analyze_chan(element_current_df)
element_current_analysis = analyze_chan(element_current_df, symbol, element_timeframe)
# 直接使用分析结果，无需复杂的时间筛选
filtered_bi_list = element_current_analysis['bi_list']
@@ -1614,7 +1648,7 @@ def analyze():
df = add_indicators(df)
# 进行缠论分析
analysis_result = analyze_chan(df)
analysis_result = analyze_chan(df, symbol, timeframe)
# 计算MACD
macd_data = calculate_macd(df)
@@ -1717,7 +1751,9 @@ def analyze():
'fx_confirmed': bool(point['fx_confirmed']) # 分型是否确认
} for point in analysis_result['klu_fx_info']],
# 添加ChanMACD分析数据
'chan_macd': serialize_chan_macd_data(analysis_result.get('chan_macd', {}), client_tz)
'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', {})
})
# 如果生成了回放数据，添加到返回结果中
@@ -1734,7 +1770,7 @@ def analyze():
element_df = add_indicators(element_df)
# 对小周期数据进行缠论分析
element_analysis = analyze_chan(element_df)
element_analysis = analyze_chan(element_df, symbol, element_timeframe)
# 计算小周期MACD数据
element_macd_data = calculate_macd(element_df)
@@ -1936,7 +1972,7 @@ def test_element_data():
# 分析次周期数据
element_df = add_indicators(element_df)
element_analysis = analyze_chan(element_df)
element_analysis = analyze_chan(element_df, symbol, element_timeframe)
return jsonify({
'main_data_count': len(main_df),
@@ -2123,7 +2159,7 @@ def filter_stocks():
continue
# 进行缠论分析
analysis_result = analyze_chan(df)
analysis_result = analyze_chan(df, symbol, timeframe)
if not analysis_result or 'klc_fx_info' not in analysis_result:
continue
web/templates/index.html
+201 -2
View File
@@ -1788,6 +1788,7 @@
elementBollingerSeries: [],
maSeries: [], // 添加均线系列
bbSeries: [], // 添加布林带系列
ema52Series: [], // 添加EMA52系列数组
chanMacdLineSeries: null, // ChanMACD线
chanMacdSignalSeries: null, // ChanMACD信号线
chanMacdHistSeries: null, // ChanMACD柱状图
@@ -2426,7 +2427,8 @@
mainBollingerSeries: [],
elementBollingerSeries: [],
maSeries: [], // 添加均线系列数组
bbSeries: [] // 添加布林带系列数组
bbSeries: [], // 添加布林带系列数组
ema52Series: [] // 添加EMA52系列数组
},
state: {
isInitialized: false,
@@ -5680,6 +5682,11 @@
displayTradePoints();
}
// 更新EMA52显示
if (currentData) {
updateEMA52Display(currentData);
}
console.log('图表初始化完成');
} catch (e) {
console.error('图表初始化错误:', e);
@@ -5912,6 +5919,9 @@
// 重新显示笔、线段和中枢等图形
redrawFractalElements();
// 更新EMA52显示
updateEMA52Display(currentData);
// 恢复之前的可视范围 - 优先使用visibleRange以确保时间轴对齐
if (tvWidget.mainChart) {
if (tvWidget.state.visibleRange) {
@@ -7121,6 +7131,8 @@
// 先销毁现有图表实例
if (tvWidget.mainChart) {
try {
// 清理EMA52系列
clearEMA52Series();
// 销毁主图表及其关联的线系列
tvWidget.mainChart = null;
tvWidget.volumeChart = null;
@@ -7147,7 +7159,8 @@
mainBollingerSeries: [],
elementBollingerSeries: [],
maSeries: [], // 添加均线系列
bbSeries: [] // 添加布林带系列
bbSeries: [], // 添加布林带系列
ema52Series: [] // 添加EMA52系列数组
};
} catch (e) {
console.error('销毁图表错误:', e);
@@ -7576,6 +7589,12 @@
// 更新表格数据
updateTables(data);
// 只有在图表已初始化且调用了updateTradingViewData时才不需要重复调用EMA52显示
// 如果图表未初始化，调用了initTradingView，那么EMA52显示已经在initTradingView中处理了
// 但为了确保在所有情况下都能正确显示，这里统一调用一次
if (currentData && currentData.ema52_dict) {
updateEMA52Display(currentData);
}
}
@@ -8653,6 +8672,186 @@
let bollingerBands = []; // 存储所有布林带配置
let bbIdCounter = 0; // 布林带ID计数器
// 清理EMA52系列
function clearEMA52Series() {
// 清理所有EMA52相关系列（包括线系列和标记系列）
if (tvWidget && tvWidget.series && tvWidget.series.ema52Series) {
tvWidget.series.ema52Series.forEach(series => {
try {
if (tvWidget.mainChart) {
tvWidget.mainChart.removeSeries(series);
}
} catch (e) {
console.warn('移除EMA52系列失败:', e);
}
});
tvWidget.series.ema52Series = [];
}
console.log('✅ EMA52系列已清理');
}
// 存储上次的EMA52数据，用于比较
let lastEMA52Data = null;
// 更新EMA52显示
function updateEMA52Display(data) {
console.log('更新EMA52显示', data.ema52_dict);
// 检查是否有EMA52数据
if (!data.ema52_dict || Object.keys(data.ema52_dict).length === 0) {
// 即使没有数据也要清理之前的系列
clearEMA52Series();
lastEMA52Data = null;
return;
}
// 检查数据是否与上次相同，如果相同则跳过更新
const currentDataStr = JSON.stringify(data.ema52_dict);
if (lastEMA52Data === currentDataStr) {
console.log('EMA52数据未变化，跳过更新');
return;
}
// 清除之前的EMA52线系列
clearEMA52Series();
// 保存当前数据
lastEMA52Data = currentDataStr;
// 定义时间周期的显示顺序和颜色
const timeframeColors = {
'1m': '#FF0000', // 红色
'3m': '#FF6600', // 橙红色
'5m': '#FF9900', // 橙色
'15m': '#FFCC00', // 黄色
'30m': '#99FF00', // 黄绿色
'1h': '#00FF00', // 绿色
'2h': '#00FF99', // 青绿色
'4h': '#00FFFF', // 青色
'6h': '#0099FF', // 蓝青色
'8h': '#0066FF', // 蓝色
'12h': '#3300FF', // 蓝紫色
'16h': '#6600FF', // 紫色
'1d': '#9900FF', // 紫红色
'2d': '#CC00FF', // 品红色
'3d': '#FF00CC', // 粉红色
'1w': '#FF0099', // 玫瑰色
'2w': '#FF3366' // 深粉色
};
// 按照预定义顺序排列时间周期
const orderedTimeframes = ['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d', '1w', '2w'];
// 获取主图表容器
const chartContainer = document.getElementById('tradingview_chart');
if (!chartContainer || !tvWidget.mainChart) {
return;
}
orderedTimeframes.forEach(timeframe => {
if (data.ema52_dict[timeframe] !== undefined && data.ema52_dict[timeframe] !== null) {
const value = data.ema52_dict[timeframe];
const color = timeframeColors[timeframe] || '#800080';
// 添加虚线到主图表
if (tvWidget.mainChart) {
try {
// 使用LightweightCharts的addLineSeries创建虚线
const lineSeries = tvWidget.mainChart.addLineSeries({
color: color,
lineWidth: 1,
lineStyle: 2, // 虚线样式
title: `EMA52-${timeframe}`,
lastValueVisible: false, // 不在价格标尺显示数值
priceLineVisible: false, // 不显示默认价格线
crosshairMarkerVisible: false,
priceFormat: {
type: 'price',
precision: 2,
minMove: 0.01,
},
priceScaleId: 'right',
});
// 创建横线数据（使用K线数据的时间范围）
if (data.kline_data && data.kline_data.length > 0) {
const firstKline = data.kline_data[0];
const lastKline = data.kline_data[data.kline_data.length - 1];
const startTime = Math.floor(new Date(firstKline.date).getTime() / 1000);
const endTime = Math.floor(new Date(lastKline.date).getTime() / 1000);
const lineData = [
{ time: startTime, value: value },
{ time: endTime, value: value }
];
lineSeries.setData(lineData);
// 使用标记在K线右侧显示文字
if (data.kline_data && data.kline_data.length > 0) {
const lastKline = data.kline_data[data.kline_data.length - 1];
const lastTime = Math.floor(new Date(lastKline.date).getTime() / 1000);
// 计算时间间隔（用于右偏移）
let timeInterval = 60; // 默认1分钟
if (data.kline_data.length > 1) {
const secondLastKline = data.kline_data[data.kline_data.length - 2];
const secondLastTime = Math.floor(new Date(secondLastKline.date).getTime() / 1000);
timeInterval = lastTime - secondLastTime;
}
// 创建右偏移的时间点（在最后一根K线右边）
const rightOffsetTime = lastTime + timeInterval * 0.3;
// 创建一个新的线系列用于显示文字标记
const markerSeries = tvWidget.mainChart.addLineSeries({
color: 'transparent',
lineWidth: 0,
lastValueVisible: false,
priceLineVisible: false,
crosshairMarkerVisible: false,
priceScaleId: 'right',
});
// 添加一个透明的数据点在右偏移位置
markerSeries.setData([{
time: rightOffsetTime,
value: value
}]);
// 在右偏移位置添加文字标记
markerSeries.setMarkers([{
time: rightOffsetTime,
position: 'inBar',
color: color,
shape: 'square',
text: `${timeframe} ${value.toFixed(2)}`,
size: 1,
}]);
// 保存标记系列引用
if (!tvWidget.series.ema52Series) {
tvWidget.series.ema52Series = [];
}
tvWidget.series.ema52Series.push(markerSeries);
}
}
// 保存系列引用以便后续清理
if (!tvWidget.series.ema52Series) {
tvWidget.series.ema52Series = [];
}
tvWidget.series.ema52Series.push(lineSeries);
} catch (e) {
console.warn('添加EMA52虚线失败:', timeframe, e);
}
}
}
});
}
// 获取随机颜色
function getRandomColor() {
const colors = ['#2962FF', '#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4',