QUANT optimize
This commit is contained in:
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@ -49,6 +49,7 @@ import core.logger as logging
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import pandas as pd
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import numpy as np
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import talib as tb
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from typing import List, Tuple
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from talib import MA_Type
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logger = logging.logger
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@ -306,6 +307,40 @@ class MetricsCalculation:
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return data
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def calculate_percentile_indicators(
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self,
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data: pd.DataFrame,
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window_size: int = 50,
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price_column: str = "close",
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percentiles: List[Tuple[float, str]] = [(0.8, "80"), (0.2, "20"), (0.9, "90"), (0.1, "10")]
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) -> pd.DataFrame:
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"""
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计算分位数指标
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:param data: 数据DataFrame
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:param window_size: 窗口大小
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:param percentiles: 分位数配置列表，格式为[(分位数, 名称后缀)]
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:return: 包含分位数指标的DataFrame
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"""
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for percentile, suffix in percentiles:
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# 计算分位数
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data[f"{price_column}_{suffix}_percentile"] = (
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data[price_column].rolling(window=window_size, min_periods=1).quantile(percentile)
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)
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# 判断价格是否达到分位数
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if suffix in ["80", "90"]:
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# 高点分位数
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data[f"{price_column}_{suffix}_high"] = (
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data[price_column] >= data[f"{price_column}_{suffix}_percentile"]
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).astype(int)
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else:
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# 低点分位数
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data[f"{price_column}_{suffix}_low"] = (
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data[price_column] <= data[f"{price_column}_{suffix}_percentile"]
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).astype(int)
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return data
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def update_macd_divergence_column(self, df: pd.DataFrame):
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"""
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更新整个DataFrame的macd_divergence列
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@ -1217,3 +1252,159 @@ class MetricsCalculation:
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avg_spacing = (spacing_5_10 + spacing_10_20 + spacing_20_30) / 3
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return avg_spacing
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def get_peaks_valleys_mean(self, data: pd.DataFrame):
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"""计算上涨波峰和下跌波谷的均值与中位数"""
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# 确保输入数据包含必要的列
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if not all(col in data.columns for col in ["open", "high", "low", "close"]):
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raise ValueError(
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"DataFrame must contain 'open', 'high', 'low', 'close' columns"
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)
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if len(data) < 100:
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return None, None
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window = 5
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# 初始化结果列表
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peaks_valleys = []
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# 检测波峰（基于high价格）
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highs = data["high"]
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for i in range(window, len(data) - window):
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if i + window >= len(data):
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break
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# 当前K线的high价格
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current_high = highs.iloc[i]
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# 窗口内的前后K线的high价格
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window_highs = highs.iloc[i - window : i + window + 1]
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# 如果当前high是窗口内的最大值，标记为波峰
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if (
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current_high == window_highs.max()
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and current_high > highs.iloc[i - 1]
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and current_high > highs.iloc[i + 1]
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):
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peaks_valleys.append(
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{
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"symbol": data.iloc[i]["symbol"],
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"bar": data.iloc[i]["bar"],
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"timestamp": data.iloc[i]["timestamp"],
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"date_time": data.iloc[i]["date_time"],
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"price": current_high,
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"type": "peak",
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}
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)
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# 检测波谷（基于low价格）
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lows = data["low"]
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for i in range(window, len(data) - window):
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if i + window >= len(data):
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break
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# 当前K线的low价格
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current_low = lows.iloc[i]
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# 窗口内的前后K线的low价格
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window_lows = lows.iloc[i - window : i + window + 1]
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# 如果当前low是窗口内的最小值，标记为波谷
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if (
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current_low == window_lows.min()
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and current_low < lows.iloc[i - 1]
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and current_low < lows.iloc[i + 1]
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):
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peaks_valleys.append(
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{
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"symbol": data.iloc[i]["symbol"],
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"bar": data.iloc[i]["bar"],
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"timestamp": data.iloc[i]["timestamp"],
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"date_time": data.iloc[i]["date_time"],
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"price": current_low,
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"type": "valley",
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}
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)
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# 转换为DataFrame并按时间排序
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result_df = pd.DataFrame(peaks_valleys)
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if not result_df.empty:
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result_df = result_df.sort_values(by="timestamp").reset_index(drop=True)
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else:
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result_df = pd.DataFrame(
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columns=["symbol", "timestamp", "date_time", "bar", "price", "type"]
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)
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# 检查result_df，如果type为peak时，下一条数据type依然为peak，则删除当前数据
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if not result_df.empty:
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# 使用布尔索引来标记要删除的行
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to_drop_peaks = []
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handled_indexes = []
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for i in range(len(result_df) - 1):
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if i in handled_indexes:
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continue
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if result_df.iloc[i]["type"] == "peak":
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current_peak_value = result_df.iloc[i]["price"]
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current_peak_index = i
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# 如type连续为peak，只应该保留price最大的行，删除其他行
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# 如type连续为peak且存在price为8 7 10 9 8 11 10的情况，只应该保留price为11的行
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for j in range(i + 1, len(result_df)):
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if result_df.iloc[j]["type"] == "peak":
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next_peak_value = result_df.iloc[j]["price"]
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if current_peak_value > next_peak_value:
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to_drop_peaks.append(j)
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else:
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to_drop_peaks.append(current_peak_index)
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current_peak_value = next_peak_value
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current_peak_index = j
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handled_indexes.append(j)
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else:
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break
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# 删除标记的行
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result_df = result_df.drop(to_drop_peaks).reset_index(drop=True)
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# 如type连续为valley，只应该保留price最小的行，删除其他行
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# 如type连续为valley且存在price为8 7 10 9 8的情况，只应该保留price为7的行
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to_drop_valleys = []
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handled_indexes = []
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for i in range(len(result_df) - 1):
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if i in handled_indexes:
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continue
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if result_df.iloc[i]["type"] == "valley":
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current_valley_value = result_df.iloc[i]["price"]
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current_valley_index = i
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for j in range(i + 1, len(result_df)):
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if result_df.iloc[j]["type"] == "valley":
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next_valley_value = result_df.iloc[j]["price"]
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if current_valley_value < next_valley_value:
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to_drop_valleys.append(j)
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else:
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to_drop_valleys.append(current_valley_index)
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current_valley_value = next_valley_value
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current_valley_index = j
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handled_indexes.append(j)
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else:
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break
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# 删除标记的行
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result_df = result_df.drop(to_drop_valleys).reset_index(drop=True)
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# 初始化价格变化列
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result_df["price_change"] = 0.0
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result_df["price_change_ratio"] = 0.0
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# 计算下一条数据与当前数据之间的价格差，并计算价格差与当前数据价格的比率
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peaks_mean = None
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valleys_mean = None
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if len(result_df) > 1:
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for i in range(len(result_df) - 1):
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result_df.iloc[i + 1, result_df.columns.get_loc("price_change")] = (
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result_df.iloc[i + 1]["price"] - result_df.iloc[i]["price"]
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)
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result_df.iloc[
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i + 1, result_df.columns.get_loc("price_change_ratio")
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] = (
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result_df.iloc[i + 1]["price_change"] / result_df.iloc[i]["price"]
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) * 100
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# peaks mean为result_df中price_change_ratio > 0的price_change_ratio的均值与中位数
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peaks_mean = abs(float(result_df[result_df["price_change_ratio"] > 0]["price_change_ratio"].mean()))
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peaks_median = abs(float(result_df[result_df["price_change_ratio"] > 0]["price_change_ratio"].median()))
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# valleys mean为result_df中price_change_ratio < 0的price_change_ratio的均值与中位数
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valleys_mean = abs(float(result_df[result_df["price_change_ratio"] < 0]["price_change_ratio"].mean()))
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valleys_median = abs(float(result_df[result_df["price_change_ratio"] < 0]["price_change_ratio"].median()))
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result = {"peaks_valleys_data": result_df, "peaks_mean": peaks_mean, "peaks_median": peaks_median, "valleys_mean": valleys_mean, "valleys_median": valleys_median}
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return result
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Binary file not shown.
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@ -52,8 +52,18 @@ class MaBreakStatistics:
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is_astock: bool = False,
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is_aindex: bool = False,
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is_binance: bool = True,
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buy_by_long_period: dict = {"by_week": False, "by_month": False},
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long_period_condition: dict = {"ma5>ma10": True, "ma10>ma20": False, "macd_diff>0": True, "macd>0": True},
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buy_by_long_period: dict = {
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"by_week": False,
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"by_month": False,
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"buy_by_10_percentile": False,
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},
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long_period_condition: dict = {
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"ma5>ma10": True,
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"ma10>ma20": False,
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"macd_diff>0": True,
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"macd>0": True,
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},
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cut_loss_by_valleys_median: bool = False,
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commission_per_share: float = 0.0008,
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):
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if is_astock or is_aindex:
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@ -146,6 +156,8 @@ class MaBreakStatistics:
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self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)
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self.buy_by_long_period = buy_by_long_period
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self.long_period_condition = long_period_condition
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self.cut_loss_by_valleys_median = cut_loss_by_valleys_median
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self.metrics_calculation = MetricsCalculation()
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def get_trade_strategy_config(self):
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with open("./json/trade_strategy.json", "r", encoding="utf-8") as f:
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@ -160,6 +172,8 @@ class MaBreakStatistics:
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by_long_period += "1W"
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if by_month:
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by_long_period += "1M"
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if self.buy_by_long_period.get("buy_by_10_percentile", False):
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by_long_period += "_10percentile"
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if by_long_period == "":
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return "no_long_period_judge"
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by_condition = ""
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@ -173,61 +187,65 @@ class MaBreakStatistics:
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by_condition += "_macdgt0"
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return by_long_period + "_" + by_condition
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def batch_statistics(self, strategy_name: str = "全均线策略"):
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if self.is_us_stock:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
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)
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main_folder = "./output/trade_sandbox/ma_strategy/us_stock/"
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if self.cut_loss_by_valleys_median:
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main_folder += "cut_loss_by_valleys_median/"
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else:
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main_folder += "no_cut_loss_by_valleys_median/"
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self.stats_output_dir = f"{main_folder}excel/{strategy_name}/"
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self.stats_chart_dir = f"{main_folder}chart/{strategy_name}/"
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elif self.is_binance:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
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)
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main_folder = "./output/trade_sandbox/ma_strategy/binance/"
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if self.cut_loss_by_valleys_median:
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main_folder += "cut_loss_by_valleys_median/"
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else:
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main_folder += "no_cut_loss_by_valleys_median/"
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self.stats_output_dir = f"{main_folder}excel/{strategy_name}/"
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self.stats_chart_dir = f"{main_folder}chart/{strategy_name}/"
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elif self.is_astock:
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long_period_desc = self.get_by_long_period_desc()
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main_folder = "./output/trade_sandbox/ma_strategy/astock/"
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if self.cut_loss_by_valleys_median:
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main_folder += "cut_loss_by_valleys_median/"
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else:
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main_folder += "no_cut_loss_by_valleys_median/"
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if len(long_period_desc) > 0:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/astock/{long_period_desc}/excel/{strategy_name}/"
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f"{main_folder}{long_period_desc}/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/astock/{long_period_desc}/chart/{strategy_name}/"
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f"{main_folder}{long_period_desc}/chart/{strategy_name}/"
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)
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else:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/astock/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/astock/chart/{strategy_name}/"
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)
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self.stats_output_dir = f"{main_folder}excel/{strategy_name}/"
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self.stats_chart_dir = f"{main_folder}chart/{strategy_name}/"
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elif self.is_aindex:
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main_folder = "./output/trade_sandbox/ma_strategy/aindex/"
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if self.cut_loss_by_valleys_median:
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main_folder += "cut_loss_by_valleys_median/"
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else:
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main_folder += "no_cut_loss_by_valleys_median/"
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long_period_desc = self.get_by_long_period_desc()
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if len(long_period_desc) > 0:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/aindex/{long_period_desc}/excel/{strategy_name}/"
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f"{main_folder}{long_period_desc}/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/aindex/{long_period_desc}/chart/{strategy_name}/"
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f"{main_folder}{long_period_desc}/chart/{strategy_name}/"
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)
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else:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/aindex/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/aindex/chart/{strategy_name}/"
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)
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self.stats_output_dir = f"{main_folder}excel/{strategy_name}/"
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self.stats_chart_dir = f"{main_folder}chart/{strategy_name}/"
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else:
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
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)
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main_folder = "./output/trade_sandbox/ma_strategy/okx/"
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if self.cut_loss_by_valleys_median:
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main_folder += "cut_loss_by_valleys_median/"
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else:
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main_folder += "no_cut_loss_by_valleys_median/"
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self.stats_output_dir = f"{main_folder}excel/{strategy_name}/"
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self.stats_chart_dir = f"{main_folder}chart/{strategy_name}/"
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os.makedirs(self.stats_output_dir, exist_ok=True)
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os.makedirs(self.stats_chart_dir, exist_ok=True)
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@ -275,39 +293,53 @@ class MaBreakStatistics:
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by="end_timestamp", ascending=True, inplace=True
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)
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symbol_bar_data.reset_index(drop=True, inplace=True)
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initial_capital = int(market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"initial_capital",
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].values[0])
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final_account_value = float(symbol_bar_data["end_account_value"].iloc[-1])
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account_value_chg = (final_account_value - initial_capital) / initial_capital * 100
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initial_capital = int(
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market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"initial_capital",
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].values[0]
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)
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final_account_value = float(
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symbol_bar_data["end_account_value"].iloc[-1]
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)
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account_value_chg = (
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(final_account_value - initial_capital)
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/ initial_capital
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* 100
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)
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account_value_chg = round(account_value_chg, 4)
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market_pct_chg = market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"pct_chg",
|
||||
].values[0]
|
||||
total_buy_commission = float(symbol_bar_data["buy_commission"].sum())
|
||||
total_sell_commission = float(symbol_bar_data["sell_commission"].sum())
|
||||
total_buy_commission = float(
|
||||
symbol_bar_data["buy_commission"].sum()
|
||||
)
|
||||
total_sell_commission = float(
|
||||
symbol_bar_data["sell_commission"].sum()
|
||||
)
|
||||
total_commission = total_buy_commission + total_sell_commission
|
||||
total_commission = round(total_commission, 4)
|
||||
total_buy_commission = round(total_buy_commission, 4)
|
||||
total_sell_commission = round(total_sell_commission, 4)
|
||||
symbol_name = str(symbol_bar_data["symbol_name"].iloc[0])
|
||||
account_value_chg_list.append({
|
||||
"strategy_name": strategy_name,
|
||||
"symbol": symbol,
|
||||
"symbol_name": symbol_name,
|
||||
"bar": bar,
|
||||
"total_buy_commission": total_buy_commission,
|
||||
"total_sell_commission": total_sell_commission,
|
||||
"total_commission": total_commission,
|
||||
"initial_account_value": initial_capital,
|
||||
"final_account_value": final_account_value,
|
||||
"account_value_chg": account_value_chg,
|
||||
"market_pct_chg": market_pct_chg,
|
||||
})
|
||||
account_value_chg_list.append(
|
||||
{
|
||||
"strategy_name": strategy_name,
|
||||
"symbol": symbol,
|
||||
"symbol_name": symbol_name,
|
||||
"bar": bar,
|
||||
"total_buy_commission": total_buy_commission,
|
||||
"total_sell_commission": total_sell_commission,
|
||||
"total_commission": total_commission,
|
||||
"initial_account_value": initial_capital,
|
||||
"final_account_value": final_account_value,
|
||||
"account_value_chg": account_value_chg,
|
||||
"market_pct_chg": market_pct_chg,
|
||||
}
|
||||
)
|
||||
account_value_chg_df = pd.DataFrame(account_value_chg_list)
|
||||
account_value_chg_df = account_value_chg_df[
|
||||
[
|
||||
|
|
@ -326,7 +358,9 @@ class MaBreakStatistics:
|
|||
]
|
||||
|
||||
account_value_statistics_df = (
|
||||
ma_break_market_data.groupby(["symbol", "symbol_name", "bar"])["end_account_value"]
|
||||
ma_break_market_data.groupby(["symbol", "symbol_name", "bar"])[
|
||||
"end_account_value"
|
||||
]
|
||||
.agg(
|
||||
account_value_max="max",
|
||||
account_value_min="min",
|
||||
|
|
@ -355,7 +389,9 @@ class MaBreakStatistics:
|
|||
|
||||
# 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count
|
||||
interval_minutes_df = (
|
||||
ma_break_market_data.groupby(["symbol", "symbol_name", "bar"])["interval_minutes"]
|
||||
ma_break_market_data.groupby(["symbol", "symbol_name", "bar"])[
|
||||
"interval_minutes"
|
||||
]
|
||||
.agg(
|
||||
interval_minutes_max="max",
|
||||
interval_minutes_min="min",
|
||||
|
|
@ -404,7 +440,9 @@ class MaBreakStatistics:
|
|||
ma_break_market_data.to_excel(
|
||||
writer, sheet_name="买卖记录明细", index=False
|
||||
)
|
||||
account_value_chg_df.to_excel(writer, sheet_name="资产价值变化", index=False)
|
||||
account_value_chg_df.to_excel(
|
||||
writer, sheet_name="资产价值变化", index=False
|
||||
)
|
||||
account_value_statistics_df.to_excel(
|
||||
writer, sheet_name="买卖账户价值统计", index=False
|
||||
)
|
||||
|
|
@ -412,7 +450,9 @@ class MaBreakStatistics:
|
|||
writer, sheet_name="买卖时间间隔统计", index=False
|
||||
)
|
||||
|
||||
chart_dict = self.draw_quant_pct_chg_bar_chart(account_value_chg_df, strategy_name)
|
||||
chart_dict = self.draw_quant_pct_chg_bar_chart(
|
||||
account_value_chg_df, strategy_name
|
||||
)
|
||||
self.output_chart_to_excel(output_file_path, chart_dict)
|
||||
chart_dict = self.draw_quant_line_chart(
|
||||
ma_break_market_data, market_data_pct_chg_df, strategy_name
|
||||
|
|
@ -540,6 +580,8 @@ class MaBreakStatistics:
|
|||
strategy_name=strategy_name,
|
||||
row=row,
|
||||
behavior="buy",
|
||||
buy_price=None,
|
||||
window_100_valleys_median=None,
|
||||
)
|
||||
|
||||
if buy_condition:
|
||||
|
|
@ -574,10 +616,21 @@ class MaBreakStatistics:
|
|||
ma_break_market_data_pair["begin_account_value"] = account_value
|
||||
continue
|
||||
else:
|
||||
valleys_median = None
|
||||
if self.cut_loss_by_valleys_median and index >= 100:
|
||||
window_100_records = market_data.iloc[index - 100 : index]
|
||||
peaks_valleys = self.metrics_calculation.get_peaks_valleys_mean(
|
||||
window_100_records
|
||||
)
|
||||
valleys_median = peaks_valleys.get("valleys_median", None)
|
||||
if valleys_median is not None and valleys_median > 0:
|
||||
valleys_median = valleys_median / 100
|
||||
sell_condition = self.fit_strategy(
|
||||
strategy_name=strategy_name,
|
||||
row=row,
|
||||
behavior="sell",
|
||||
buy_price=ma_break_market_data_pair["begin_close"],
|
||||
window_100_valleys_median=valleys_median,
|
||||
)
|
||||
|
||||
if sell_condition or index == len(market_data) - 1:
|
||||
|
|
@ -612,6 +665,12 @@ class MaBreakStatistics:
|
|||
ma_break_market_data_pair["pct_chg"] = round(
|
||||
ma_break_market_data_pair["pct_chg"] * 100, 4
|
||||
)
|
||||
if valleys_median is not None:
|
||||
ma_break_market_data_pair["valleys_median"] = (
|
||||
valleys_median * 100
|
||||
)
|
||||
else:
|
||||
ma_break_market_data_pair["valleys_median"] = None
|
||||
ma_break_market_data_pair["profit_loss"] = profit_loss
|
||||
ma_break_market_data_pair["sell_commission"] = sell_commission
|
||||
ma_break_market_data_pair["end_account_value"] = account_value
|
||||
|
|
@ -804,7 +863,12 @@ class MaBreakStatistics:
|
|||
current_end_date_str = current_end_date.strftime("%Y-%m-%d")
|
||||
logger.info(f"获取{symbol}数据：{start_date_str}至{current_end_date_str}")
|
||||
current_data = self.db_market_data.query_market_data_by_symbol_bar(
|
||||
symbol, bar, fields, start=start_date_str, end=current_end_date_str, table_name=table_name
|
||||
symbol,
|
||||
bar,
|
||||
fields,
|
||||
start=start_date_str,
|
||||
end=current_end_date_str,
|
||||
table_name=table_name,
|
||||
)
|
||||
if current_data is not None and len(current_data) > 0:
|
||||
current_data = pd.DataFrame(current_data)
|
||||
|
|
@ -843,12 +907,16 @@ class MaBreakStatistics:
|
|||
if len(end_date) != 10:
|
||||
end_date = self.change_date_format(end_date)
|
||||
if bar == "1M":
|
||||
# 获取上两个月的日期
|
||||
last_date = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=60)
|
||||
# 获取上五年的日期
|
||||
last_date = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(
|
||||
days=360 * 5
|
||||
)
|
||||
last_date = last_date.strftime("%Y-%m-%d")
|
||||
elif bar == "1W":
|
||||
# 获取上两周的日期
|
||||
last_date = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=14)
|
||||
# 获取上两年的日期
|
||||
last_date = datetime.strptime(end_date, "%Y-%m-%d") - timedelta(
|
||||
days=360 * 2
|
||||
)
|
||||
last_date = last_date.strftime("%Y-%m-%d")
|
||||
else:
|
||||
last_date = None
|
||||
|
|
@ -856,43 +924,50 @@ class MaBreakStatistics:
|
|||
if len(table_name) == 0 or last_date is None:
|
||||
return None
|
||||
fields = [
|
||||
"a.ts_code as symbol",
|
||||
"b.name as symbol_name",
|
||||
f"'{bar}' as bar",
|
||||
"0 as timestamp",
|
||||
"trade_date as date_time",
|
||||
"open",
|
||||
"high",
|
||||
"low",
|
||||
"close",
|
||||
"vol as volume",
|
||||
"MA5 as ma5",
|
||||
"MA10 as ma10",
|
||||
"MA20 as ma20",
|
||||
"MA30 as ma30",
|
||||
"均线交叉 as ma_cross",
|
||||
"DIF as dif",
|
||||
"DEA as dea",
|
||||
"MACD as macd",
|
||||
]
|
||||
"a.ts_code as symbol",
|
||||
"b.name as symbol_name",
|
||||
f"'{bar}' as bar",
|
||||
"0 as timestamp",
|
||||
"trade_date as date_time",
|
||||
"open",
|
||||
"high",
|
||||
"low",
|
||||
"close",
|
||||
"vol as volume",
|
||||
"MA5 as ma5",
|
||||
"MA10 as ma10",
|
||||
"MA20 as ma20",
|
||||
"MA30 as ma30",
|
||||
"均线交叉 as ma_cross",
|
||||
"DIF as dif",
|
||||
"DEA as dea",
|
||||
"MACD as macd",
|
||||
]
|
||||
data = self.db_market_data.query_market_data_by_symbol_bar(
|
||||
symbol, bar, fields, start=last_date, end=end_date, table_name=table_name
|
||||
)
|
||||
if data is not None and len(data) > 0:
|
||||
data = pd.DataFrame(data)
|
||||
data.sort_values(by="date_time", inplace=True)
|
||||
data = self.metrics_calculation.calculate_percentile_indicators(
|
||||
data=data,
|
||||
window_size=50,
|
||||
price_column="close",
|
||||
percentiles=[(0.1, "10")],
|
||||
)
|
||||
latest_row = data.iloc[-1]
|
||||
if (latest_row["ma5"] is None or
|
||||
latest_row["ma10"] is None or
|
||||
latest_row["ma20"] is None or
|
||||
latest_row["dif"] is None or
|
||||
latest_row["macd"] is None):
|
||||
if (
|
||||
latest_row["ma5"] is None
|
||||
or latest_row["ma10"] is None
|
||||
or latest_row["ma20"] is None
|
||||
or latest_row["dif"] is None
|
||||
or latest_row["macd"] is None
|
||||
):
|
||||
return None
|
||||
return latest_row
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def update_data(self, data: pd.DataFrame):
|
||||
"""
|
||||
更新数据
|
||||
|
|
@ -902,10 +977,13 @@ class MaBreakStatistics:
|
|||
:param data: 数据
|
||||
:return: 更新后的数据
|
||||
"""
|
||||
data["date_time"] = data["date_time"].apply(lambda x: self.change_date_format(x))
|
||||
data["timestamp"] = data["date_time"].apply(lambda x: transform_date_time_to_timestamp(x))
|
||||
metrics_calculation = MetricsCalculation()
|
||||
data = metrics_calculation.ma5102030(data)
|
||||
data["date_time"] = data["date_time"].apply(
|
||||
lambda x: self.change_date_format(x)
|
||||
)
|
||||
data["timestamp"] = data["date_time"].apply(
|
||||
lambda x: transform_date_time_to_timestamp(x)
|
||||
)
|
||||
data = self.metrics_calculation.ma5102030(data)
|
||||
return data
|
||||
|
||||
def change_date_format(self, date_text: str):
|
||||
|
|
@ -920,7 +998,23 @@ class MaBreakStatistics:
|
|||
strategy_name: str = "全均线策略",
|
||||
row: pd.Series = None,
|
||||
behavior: str = "buy",
|
||||
buy_price: float = None,
|
||||
window_100_valleys_median: float = None,
|
||||
):
|
||||
# 如果行为是卖出，则判断是否根据止损价格卖出
|
||||
# 止损价格 = 买入价格 * (1 - window_100_valleys_median)
|
||||
# window_100_valleys_median为100日下跌波谷幅度中位数
|
||||
# 当前价格 < 止损价格，则卖出
|
||||
if (
|
||||
behavior == "sell"
|
||||
and buy_price is not None
|
||||
and window_100_valleys_median is not None
|
||||
):
|
||||
current_price = float(row["close"])
|
||||
if current_price < buy_price:
|
||||
loss_ratio = (buy_price - current_price) / buy_price
|
||||
if loss_ratio > window_100_valleys_median:
|
||||
return True
|
||||
strategy_config = self.main_strategy.get(strategy_name, None)
|
||||
if strategy_config is None:
|
||||
logger.error(f"策略{strategy_name}不存在")
|
||||
|
|
@ -953,21 +1047,56 @@ class MaBreakStatistics:
|
|||
long_period_condition_list.append("macd>0")
|
||||
if len(long_period_condition_list) > 0:
|
||||
if self.buy_by_long_period.get("by_week", False):
|
||||
long_period_data = self.get_long_period_data(row["symbol"], "1W", date_time)
|
||||
long_period_data = self.get_long_period_data(
|
||||
row["symbol"], "1W", date_time
|
||||
)
|
||||
if long_period_data is not None:
|
||||
condition = self.get_judge_result(long_period_data, long_period_condition_list, "and", condition)
|
||||
condition = self.get_judge_result(
|
||||
long_period_data,
|
||||
long_period_condition_list,
|
||||
"and",
|
||||
condition,
|
||||
)
|
||||
if not condition:
|
||||
logger.info(f"根据周线指标，{row['symbol']}不满足买入条件")
|
||||
# 如果周线处于空头条件，但收盘价位于50窗口的低点10分位数，则买入
|
||||
if self.buy_by_long_period.get("buy_by_10_percentile", False):
|
||||
if long_period_data["close_10_low"] == 1:
|
||||
condition = True
|
||||
if not condition:
|
||||
logger.info(
|
||||
f"根据周线指标，{row['symbol']}不满足买入条件"
|
||||
)
|
||||
if self.buy_by_long_period.get("by_month", False):
|
||||
long_period_data = self.get_long_period_data(row["symbol"], "1M", date_time)
|
||||
long_period_data = self.get_long_period_data(
|
||||
row["symbol"], "1M", date_time
|
||||
)
|
||||
if long_period_data is not None:
|
||||
condition = self.get_judge_result(long_period_data, long_period_condition_list, "and", condition)
|
||||
condition = self.get_judge_result(
|
||||
long_period_data,
|
||||
long_period_condition_list,
|
||||
"and",
|
||||
condition,
|
||||
)
|
||||
|
||||
if not condition:
|
||||
logger.info(f"根据月线指标，{row['symbol']}不满足买入条件")
|
||||
# 如果月线处于空头条件，但收盘价位于50窗口的低点10分位数，则买入
|
||||
if self.buy_by_long_period.get("buy_by_10_percentile", False):
|
||||
if long_period_data["close_10_low"] == 1:
|
||||
condition = True
|
||||
if not condition:
|
||||
logger.info(
|
||||
f"根据月线指标，{row['symbol']}不满足买入条件"
|
||||
)
|
||||
|
||||
return condition
|
||||
|
||||
def get_judge_result(self, row: pd.Series, condition_list: list, and_or: str = "and", raw_condition: bool = True):
|
||||
def get_judge_result(
|
||||
self,
|
||||
row: pd.Series,
|
||||
condition_list: list,
|
||||
and_or: str = "and",
|
||||
raw_condition: bool = True,
|
||||
):
|
||||
ma_cross = row["ma_cross"]
|
||||
if pd.isna(ma_cross) or ma_cross is None:
|
||||
ma_cross = ""
|
||||
|
|
@ -985,51 +1114,53 @@ class MaBreakStatistics:
|
|||
macd_dea = float(row["dea"])
|
||||
macd = float(row["macd"])
|
||||
if and_or == "and":
|
||||
for and_condition in condition_list:
|
||||
if and_condition == "5上穿10":
|
||||
raw_condition = raw_condition and ("5上穿10" in ma_cross)
|
||||
elif and_condition == "10上穿20":
|
||||
raw_condition = raw_condition and ("10上穿20" in ma_cross)
|
||||
elif and_condition == "20上穿30":
|
||||
raw_condition = raw_condition and ("20上穿30" in ma_cross)
|
||||
elif and_condition == "ma5>ma10":
|
||||
raw_condition = raw_condition and (ma5 > ma10)
|
||||
elif and_condition == "ma10>ma20":
|
||||
raw_condition = raw_condition and (ma10 > ma20)
|
||||
elif and_condition == "ma20>ma30":
|
||||
raw_condition = raw_condition and (ma20 > ma30)
|
||||
elif and_condition == "close>ma20":
|
||||
raw_condition = raw_condition and (close > ma20)
|
||||
elif and_condition == "volume_pct_chg>0.2" and volume_pct_chg is not None:
|
||||
raw_condition = raw_condition and (volume_pct_chg > 0.2)
|
||||
elif and_condition == "macd_diff>0":
|
||||
raw_condition = raw_condition and (macd_diff > 0)
|
||||
elif and_condition == "macd_dea>0":
|
||||
raw_condition = raw_condition and (macd_dea > 0)
|
||||
elif and_condition == "macd>0":
|
||||
raw_condition = raw_condition and (macd > 0)
|
||||
elif and_condition == "10下穿5":
|
||||
raw_condition = raw_condition and ("10下穿5" in ma_cross)
|
||||
elif and_condition == "20下穿10":
|
||||
raw_condition = raw_condition and ("20下穿10" in ma_cross)
|
||||
elif and_condition == "30下穿20":
|
||||
raw_condition = raw_condition and ("30下穿20" in ma_cross)
|
||||
elif and_condition == "ma5<ma10":
|
||||
raw_condition = raw_condition and (ma5 < ma10)
|
||||
elif and_condition == "ma10<ma20":
|
||||
raw_condition = raw_condition and (ma10 < ma20)
|
||||
elif and_condition == "ma20<ma30":
|
||||
raw_condition = raw_condition and (ma20 < ma30)
|
||||
elif and_condition == "close<ma20":
|
||||
raw_condition = raw_condition and (close < ma20)
|
||||
elif and_condition == "macd_diff<0":
|
||||
raw_condition = raw_condition and (macd_diff < 0)
|
||||
elif and_condition == "macd_dea<0":
|
||||
raw_condition = raw_condition and (macd_dea < 0)
|
||||
elif and_condition == "macd<0":
|
||||
raw_condition = raw_condition and (macd < 0)
|
||||
else:
|
||||
pass
|
||||
for and_condition in condition_list:
|
||||
if and_condition == "5上穿10":
|
||||
raw_condition = raw_condition and ("5上穿10" in ma_cross)
|
||||
elif and_condition == "10上穿20":
|
||||
raw_condition = raw_condition and ("10上穿20" in ma_cross)
|
||||
elif and_condition == "20上穿30":
|
||||
raw_condition = raw_condition and ("20上穿30" in ma_cross)
|
||||
elif and_condition == "ma5>ma10":
|
||||
raw_condition = raw_condition and (ma5 > ma10)
|
||||
elif and_condition == "ma10>ma20":
|
||||
raw_condition = raw_condition and (ma10 > ma20)
|
||||
elif and_condition == "ma20>ma30":
|
||||
raw_condition = raw_condition and (ma20 > ma30)
|
||||
elif and_condition == "close>ma20":
|
||||
raw_condition = raw_condition and (close > ma20)
|
||||
elif (
|
||||
and_condition == "volume_pct_chg>0.2" and volume_pct_chg is not None
|
||||
):
|
||||
raw_condition = raw_condition and (volume_pct_chg > 0.2)
|
||||
elif and_condition == "macd_diff>0":
|
||||
raw_condition = raw_condition and (macd_diff > 0)
|
||||
elif and_condition == "macd_dea>0":
|
||||
raw_condition = raw_condition and (macd_dea > 0)
|
||||
elif and_condition == "macd>0":
|
||||
raw_condition = raw_condition and (macd > 0)
|
||||
elif and_condition == "10下穿5":
|
||||
raw_condition = raw_condition and ("10下穿5" in ma_cross)
|
||||
elif and_condition == "20下穿10":
|
||||
raw_condition = raw_condition and ("20下穿10" in ma_cross)
|
||||
elif and_condition == "30下穿20":
|
||||
raw_condition = raw_condition and ("30下穿20" in ma_cross)
|
||||
elif and_condition == "ma5<ma10":
|
||||
raw_condition = raw_condition and (ma5 < ma10)
|
||||
elif and_condition == "ma10<ma20":
|
||||
raw_condition = raw_condition and (ma10 < ma20)
|
||||
elif and_condition == "ma20<ma30":
|
||||
raw_condition = raw_condition and (ma20 < ma30)
|
||||
elif and_condition == "close<ma20":
|
||||
raw_condition = raw_condition and (close < ma20)
|
||||
elif and_condition == "macd_diff<0":
|
||||
raw_condition = raw_condition and (macd_diff < 0)
|
||||
elif and_condition == "macd_dea<0":
|
||||
raw_condition = raw_condition and (macd_dea < 0)
|
||||
elif and_condition == "macd<0":
|
||||
raw_condition = raw_condition and (macd < 0)
|
||||
else:
|
||||
pass
|
||||
elif and_or == "or":
|
||||
for or_condition in condition_list:
|
||||
if or_condition == "5上穿10":
|
||||
|
|
@ -1046,7 +1177,9 @@ class MaBreakStatistics:
|
|||
raw_condition = raw_condition or (ma20 > ma30)
|
||||
elif or_condition == "close>ma20":
|
||||
raw_condition = raw_condition or (close > ma20)
|
||||
elif or_condition == "volume_pct_chg>0.2" and volume_pct_chg is not None:
|
||||
elif (
|
||||
or_condition == "volume_pct_chg>0.2" and volume_pct_chg is not None
|
||||
):
|
||||
raw_condition = raw_condition or (volume_pct_chg > 0.2)
|
||||
elif or_condition == "macd_diff>0":
|
||||
raw_condition = raw_condition or (macd_diff > 0)
|
||||
|
|
@ -1078,7 +1211,6 @@ class MaBreakStatistics:
|
|||
pass
|
||||
return raw_condition
|
||||
|
||||
|
||||
def draw_quant_pct_chg_bar_chart(
|
||||
self, data: pd.DataFrame, strategy_name: str = "全均线策略"
|
||||
):
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import core.logger as logging
|
|||
from datetime import datetime
|
||||
from time import sleep
|
||||
import pandas as pd
|
||||
import os
|
||||
from core.biz.market_data import MarketData
|
||||
from core.trade.ma_break_statistics import MaBreakStatistics
|
||||
from core.db.db_market_data import DBMarketData
|
||||
|
|
@ -33,7 +34,13 @@ class TradeMaStrategyMain:
|
|||
is_binance: bool = False,
|
||||
commission_per_share: float = 0,
|
||||
buy_by_long_period: dict = {"by_week": False, "by_month": False},
|
||||
long_period_condition: dict = {"ma5>ma10": False, "ma10>ma20": False, "macd_diff>0": False, "macd>0": False},
|
||||
long_period_condition: dict = {
|
||||
"ma5>ma10": False,
|
||||
"ma10>ma20": False,
|
||||
"macd_diff>0": False,
|
||||
"macd>0": False,
|
||||
},
|
||||
cut_loss_by_valleys_median: bool = False,
|
||||
):
|
||||
self.ma_break_statistics = MaBreakStatistics(
|
||||
is_us_stock=is_us_stock,
|
||||
|
|
@ -43,6 +50,7 @@ class TradeMaStrategyMain:
|
|||
commission_per_share=commission_per_share,
|
||||
buy_by_long_period=buy_by_long_period,
|
||||
long_period_condition=long_period_condition,
|
||||
cut_loss_by_valleys_median=cut_loss_by_valleys_median,
|
||||
)
|
||||
|
||||
def batch_ma_break_statistics(self):
|
||||
|
|
@ -77,41 +85,199 @@ def test_single_symbol():
|
|||
)
|
||||
symbol = "600111.SH"
|
||||
bar = "1D"
|
||||
ma_break_statistics.trade_simulate(symbol=symbol, bar=bar, strategy_name="均线macd结合策略2")
|
||||
ma_break_statistics.trade_simulate(
|
||||
symbol=symbol, bar=bar, strategy_name="均线macd结合策略2"
|
||||
)
|
||||
|
||||
|
||||
def batch_run_strategy():
|
||||
commission_per_share_list = [0, 0.0008]
|
||||
# cut_loss_by_valleys_median_list = [True, False]
|
||||
cut_loss_by_valleys_median_list = [False]
|
||||
buy_by_long_period_list = [
|
||||
# {"by_week": True, "by_month": True, "buy_by_10_percentile": True},
|
||||
{"by_week": False, "by_month": True, "buy_by_10_percentile": True},
|
||||
{"by_week": True, "by_month": False, "buy_by_10_percentile": True},
|
||||
{"by_week": False, "by_month": False, "buy_by_10_percentile": False},
|
||||
]
|
||||
# buy_by_long_period_list = [{"by_week": False, "by_month": False}]
|
||||
long_period_condition_list = [
|
||||
{"ma5>ma10": True, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
|
||||
{"ma5>ma10": True, "ma10>ma20": False, "macd_diff>0": True, "macd>0": True},
|
||||
{"ma5>ma10": False, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
|
||||
]
|
||||
|
||||
for commission_per_share in commission_per_share_list:
|
||||
for cut_loss_by_valleys_median in cut_loss_by_valleys_median_list:
|
||||
for buy_by_long_period in buy_by_long_period_list:
|
||||
for long_period_condition in long_period_condition_list:
|
||||
logger.info(
|
||||
f"开始计算, 主要参数：commission_per_share: {commission_per_share}, buy_by_long_period: {buy_by_long_period}, long_period_condition: {long_period_condition}"
|
||||
)
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(
|
||||
is_us_stock=False,
|
||||
is_astock=False,
|
||||
is_aindex=True,
|
||||
is_binance=False,
|
||||
commission_per_share=commission_per_share,
|
||||
buy_by_long_period=buy_by_long_period,
|
||||
long_period_condition=long_period_condition,
|
||||
cut_loss_by_valleys_median=cut_loss_by_valleys_median,
|
||||
)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(
|
||||
is_us_stock=False,
|
||||
is_astock=True,
|
||||
is_aindex=False,
|
||||
is_binance=False,
|
||||
commission_per_share=commission_per_share,
|
||||
buy_by_long_period=buy_by_long_period,
|
||||
long_period_condition=long_period_condition,
|
||||
cut_loss_by_valleys_median=cut_loss_by_valleys_median,
|
||||
)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
|
||||
|
||||
def pickup_data_from_excel():
|
||||
main_path = r"./output/trade_sandbox/ma_strategy"
|
||||
sub_main_paths = ["aindex", "astock"]
|
||||
fix_sub_path = "no_cut_loss_by_valleys_median"
|
||||
sub_folder = r"excel/均线macd结合策略2/"
|
||||
file_feature_name = "with_commission"
|
||||
original_df_columns = [
|
||||
"strategy_name",
|
||||
"symbol",
|
||||
"symbol_name",
|
||||
"bar",
|
||||
"total_buy_commission",
|
||||
"total_sell_commission",
|
||||
"total_commission",
|
||||
"initial_account_value",
|
||||
"final_account_value",
|
||||
"account_value_chg",
|
||||
"market_pct_chg",
|
||||
]
|
||||
original_df_list = []
|
||||
for sub_main_path in sub_main_paths:
|
||||
logger.info(f"开始读取{sub_main_path}数据")
|
||||
full_sub_main_path = os.path.join(main_path, sub_main_path, fix_sub_path)
|
||||
# 读取sub_main_path下的所有文件夹
|
||||
folder_list = os.listdir(full_sub_main_path)
|
||||
for folder in folder_list:
|
||||
logger.info(f"开始读取{folder}数据")
|
||||
folder_path = os.path.join(full_sub_main_path, folder)
|
||||
properties = get_properties_by_folder_name(folder, sub_main_path)
|
||||
logger.info(f"开始读取{folder}数据")
|
||||
# 读取folder_path的sub_folder下的所有文件
|
||||
sub_folder_path = os.path.join(folder_path, sub_folder)
|
||||
file_list = os.listdir(sub_folder_path)
|
||||
for file in file_list:
|
||||
logger.info(f"开始读取{file}数据")
|
||||
if file_feature_name in file:
|
||||
file_path = os.path.join(sub_folder_path, file)
|
||||
df = pd.read_excel(file_path, sheet_name="资产价值变化")
|
||||
logger.info(f"开始读取{file}数据")
|
||||
# 向df添加properties
|
||||
df = df.assign(**properties)
|
||||
df = df[list(properties.keys()) + original_df_columns]
|
||||
# 将df添加到original_df_list
|
||||
original_df_list.append(df)
|
||||
final_df = pd.concat(original_df_list)
|
||||
excel_folder_path = os.path.join(main_path, "aindex_astock_均线macd结合策略2")
|
||||
os.makedirs(excel_folder_path, exist_ok=True)
|
||||
excel_file_path = os.path.join(
|
||||
excel_folder_path, "all_strategy_with_commission.xlsx"
|
||||
)
|
||||
with pd.ExcelWriter(excel_file_path) as writer:
|
||||
final_df.to_excel(
|
||||
writer, sheet_name="all_strategy_with_commission", index=False
|
||||
)
|
||||
|
||||
|
||||
def get_properties_by_folder_name(folder_name: str, symbol_type: str):
|
||||
properties = {}
|
||||
sub_properties = folder_name.split("_")
|
||||
properties["symbol_type"] = symbol_type
|
||||
properties["buy_by_long_period"] = "no_long_period"
|
||||
properties["long_period_ma5gtma10"] = False
|
||||
properties["long_period_ma10gtma20"] = False
|
||||
properties["long_period_macd_diffgt0"] = False
|
||||
properties["long_period_macdgt0"] = False
|
||||
properties["buy_by_long_period_10_percentile"] = False
|
||||
if "1M" in sub_properties:
|
||||
properties["buy_by_long_period"] = "1M"
|
||||
if "1W" in sub_properties:
|
||||
properties["buy_by_long_period"] = "1W"
|
||||
if "1W1M" in sub_properties:
|
||||
properties["buy_by_long_period"] = "1W1M"
|
||||
if "ma5gtma10" in sub_properties:
|
||||
properties["long_period_ma5gtma10"] = True
|
||||
if "ma10gtma20" in sub_properties:
|
||||
properties["long_period_ma10gtma20"] = True
|
||||
if "macd_diffgt0" in sub_properties:
|
||||
properties["long_period_macd_diffgt0"] = True
|
||||
if "macdgt0" in sub_properties:
|
||||
properties["long_period_macdgt0"] = True
|
||||
if "10percentile" in sub_properties:
|
||||
properties["buy_by_long_period_10_percentile"] = True
|
||||
return properties
|
||||
|
||||
|
||||
def profit_loss_ratio():
|
||||
"""
|
||||
计算利润损失比
|
||||
公式：盈利/盈利交易次数 : 亏损/亏损交易次数
|
||||
"""
|
||||
folder = r"./output/trade_sandbox/ma_strategy/binance/excel/均线macd结合策略2/"
|
||||
prefix = ["无交易费用", "有交易费用"]
|
||||
for prefix in prefix:
|
||||
excel_file_path = os.path.join(
|
||||
folder, f"{prefix}_趋势投资_from_201708181600_to_202509020600.xlsx"
|
||||
)
|
||||
df = pd.read_excel(excel_file_path, sheet_name="买卖记录明细")
|
||||
symbol_list = list(df["symbol"].unique())
|
||||
bar_list = list(df["bar"].unique())
|
||||
data_list = []
|
||||
for symbol in symbol_list:
|
||||
for bar in bar_list:
|
||||
df_symbol_bar = df[df["symbol"] == symbol][df["bar"] == bar]
|
||||
start_date = df_symbol_bar["begin_date_time"].min()
|
||||
end_date = df_symbol_bar["end_date_time"].max()
|
||||
profit_df = df_symbol_bar[df_symbol_bar["profit_loss"] > 0]
|
||||
loss_df = df_symbol_bar[df_symbol_bar["profit_loss"] < 0]
|
||||
|
||||
profit_amount = sum(profit_df["profit_loss"])
|
||||
loss_amount = abs(sum(loss_df["profit_loss"]))
|
||||
|
||||
profit_count = len(profit_df)
|
||||
loss_count = len(loss_df)
|
||||
|
||||
if profit_count == 0 or loss_count == 0:
|
||||
continue
|
||||
|
||||
profit_loss_ratio = round(
|
||||
(profit_amount / profit_count) / (loss_amount / loss_count) * 100, 4
|
||||
)
|
||||
|
||||
data_list.append(
|
||||
{
|
||||
"币种": symbol,
|
||||
"交易周期": bar,
|
||||
"开始时间": start_date,
|
||||
"结束时间": end_date,
|
||||
"盈利金额": profit_amount,
|
||||
"盈利次数": profit_count,
|
||||
"亏损金额": loss_amount,
|
||||
"亏损次数": loss_count,
|
||||
"盈亏比": profit_loss_ratio,
|
||||
"盈亏比公式": f"盈利金额/盈利次数 : 亏损金额/亏损次数",
|
||||
}
|
||||
)
|
||||
final_df = pd.DataFrame(data_list)
|
||||
final_df.to_excel(os.path.join(folder, f"{prefix}时虚拟货币利润损失比.xlsx"), index=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
commission_per_share_list = [0, 0.0008]
|
||||
buy_by_long_period_list = [{"by_week": True, "by_month": True},
|
||||
{"by_week": True, "by_month": False},
|
||||
{"by_week": False, "by_month": True},
|
||||
{"by_week": False, "by_month": False}]
|
||||
long_period_condition_list = [{"ma5>ma10": True, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True},
|
||||
{"ma5>ma10": True, "ma10>ma20": False, "macd_diff>0": True, "macd>0": True},
|
||||
{"ma5>ma10": False, "ma10>ma20": True, "macd_diff>0": True, "macd>0": True}]
|
||||
|
||||
for commission_per_share in commission_per_share_list:
|
||||
for buy_by_long_period in buy_by_long_period_list:
|
||||
for long_period_condition in long_period_condition_list:
|
||||
logger.info(f"开始计算, 主要参数：commission_per_share: {commission_per_share}, buy_by_long_period: {buy_by_long_period}, long_period_condition: {long_period_condition}")
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(
|
||||
is_us_stock=False,
|
||||
is_astock=False,
|
||||
is_aindex=True,
|
||||
is_binance=False,
|
||||
commission_per_share=commission_per_share,
|
||||
buy_by_long_period=buy_by_long_period,
|
||||
long_period_condition=long_period_condition,
|
||||
)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(
|
||||
is_us_stock=False,
|
||||
is_astock=True,
|
||||
is_aindex=False,
|
||||
is_binance=False,
|
||||
commission_per_share=commission_per_share,
|
||||
buy_by_long_period=buy_by_long_period,
|
||||
long_period_condition=long_period_condition,
|
||||
)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
# batch_run_strategy()
|
||||
profit_loss_ratio()
|
||||
|
|
|
|||
Loading…
Reference in New Issue