数据脉冲指数Data Pulse Index
def calculate_slope_ma(time_frame, period, angle_ma_mode, angle_ma_price, k_count):
#输入
return 0.5 #
def modify_sl_and_tp(position_type, magic_number, stop_loss, take_profit):
# 模拟修改止损止盈的函数
print(f”Modify stop loss and take profit for {position_type} position with magic number {magic_number} to SL={stop_loss} TP={take_profit}”)def close_position(position_type, magic_number):
# 模拟关闭仓位的函数
print(f”Close {position_type} position with magic number {magic_number}”)
+-
def explore_alpha_factors():
li_1 = calculate_slope_ma(“H1”, H1_slope_ma_period, H1_angle_ma_mode, H1_angle_ma_price, H1_k_count)
li_0 = 0
if li_1 > H1_long_ma_angle:
li_0 = 1
if li_1 < -H1_short_ma_angle:
li_0 = 2
modify_sl_and_tp(“buy”, magic_number, stop_loss_value, take_profit_value)
close_position(“sell”, RSI_magic_number_30)
close_position(“buy”, RSI_magic_number_70)
# 其他代码段的调用
explore_alpha_factors()def f0_6_H1():
li_1 = calculate_slope_ma(“H1”, H1_slope_ma_period, H1_angle_ma_mode, H1_angle_ma_price, H1_k_count)
li_0 = 0
if li_1 > H1_long_ma_angle:
li_0 = 1
if li_1 < -H1_short_ma_angle:
li_0 = 2
return li_0
def modify_sl_and_tp(as_0, magic, sl_1, tp_1):
for i in range(OrdersTotal() – 1, -1, -1):
if OrderSelect(i, SELECT_BY_POS, MODE_TRADES):
if OrderSymbol() != Symbol() or OrderMagicNumber() != magic:
continue
if OrderSymbol() == Symbol() and OrderMagicNumber() == magic:
if as_0 == “buy” and OrderType() == OP_BUY:
if OrderStopLoss() != sl_1 and OrderTakeProfit() != tp_1:
OrderModify(OrderTicket(), OrderOpenPrice(), sl_1, tp_1, 0, Yellow)
if as_0 == “sell” and OrderType() == OP_SELL:
if OrderStopLoss() != sl_1 and OrderTakeProfit() != tp_1:
OrderModify(OrderTicket(), OrderOpenPrice(), sl_1, tp_1, 0, Yellow)
def close_f0_1():
pass
# 调用之前的代码段及新的整合函数
f0_6_result = f0_6_H1()
print(f”f0_6_H1 Result: {f0_6_result}”)
# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# 创建示例数据集
data = {
‘feature1’: np.random.rand(100),
‘feature2’: np.random.rand(100),
‘target’: 2*np.random.rand(100) + np.random.normal(size=100)
}
df = pd.DataFrame(data)
# 划分特征和目标变量
X = df[[‘feature1’, ‘feature2’]]
y = df[‘target’]
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建并训练线性回归模型
model = LinearRegression()
model.fit(X_train, y_train)
# 使用模型进行预测
predictions = model.predict(X_test)
# 评估模型性能
mse = mean_squared_error(y_test, predictions)
print(“均方误差:”, mse)
这段代码中是一个一个简单的示例数据集，包括两个特征（feature1和feature2）和一个目标变量（target）。
然后使用线性回归模型对这些特征进行训练，并用模型进行预测。