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最近比较火的OpenAI-ChatGPT，太有意思了。尝试让它写了几个策略，您别说，还真是有模有样。我们来看看吧。

模型一：均值回归模型

源码：

import numpy as np
import pandas as pd
from sklearn import linear_model
import numpy as np
import pandas as pd
from sklearn import linear_model

# 定义交易资金
capital = 100000

# 定义每次交易的数量
trade_qty = 1000

# 定义交易费用
trade_fee = 0.003

# 定义时间跨度
time_span = 10

# 计算交易收益
def calc_profit(open_price, close_price):
    return (close_price - open_price) * trade_qty - trade_qty * trade_fee * 2

# 计算交易均值回归模型
def calc_linear_model(df):
    X = np.array(df['open']).reshape(-1, 1)
    y = np.array(df['close']).reshape(-1, 1)
    model = linear_model.LinearRegression()
    model.fit(X, y)
    return model.coef_[0][0], model.intercept_[0]

# 读入交易数据
df = pd.read_csv('trade_data.csv')

# 计算交易均值回归模型参数
slope, intercept = calc_linear_model(df)

# 初始化资产和交易结果
capital_list = []
result_list = []

# 开始交易
for i in range(time_span, len(df)):
    # 计算收益
    open_price = df.iloc[i-time_span]['open']
    close_price = df.iloc[i]['close']
    profit = calc_profit(open_price, close_price)

    # 更新资产
    capital += profit

    # 记录交易结果
    capital_list.append(capital)
    result_list.append(profit)

# 输出交易结果
print('Final capital:', capital)

模型二：一个均线策略

源码：

# 导入需要的库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 导入股票数据
stock_data = pd.read_csv('stock_data.csv')

# 计算移动平均线
ma_5 = stock_data['close'].rolling(5).mean()
ma_10 = stock_data['close'].rolling(10).mean()

# 绘制移动平均线图
plt.plot(stock_data['date'], ma_5)
plt.plot(stock_data['date'], ma_10)
plt.title('Moving Average')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend(['MA5', 'MA10'])

# 定义交易策略
def trade(stock_data, ma_5, ma_10):
    # 创建交易信号数组
    signals = np.zeros(len(stock_data))
    
    # 遍历每个交易日
    for i in range(len(stock_data)):
        # 如果MA5大于MA10，则买入
        if ma_5[i] > ma_10[i]:
            signals[i] = 1
        # 如果MA5小于MA10，则卖出
        elif ma_5[i] < ma_10[i]:
            signals[i] = -1
            
    return signals

# 运行交易策略
signals = trade(stock_data, ma_5, ma_10)

# 计算持仓和收益
position = 0
profit = 0

for i in range(len(signals)):
    # 买入
    if signals[i] == 1:
        position += stock_data['close'][i]
        
    # 卖出
    elif signals[i] == -1:
        profit += stock_data['close'][i] - position
        position = 0
        
# 输出最终收益
print('Final profit:', profit)

模型三：唐奇安通道+MACD

源码：

 

import numpy as np
import talib

# 定义唐奇安通道函数
def Donchian(close, n):
    high = talib.MAX(close, n)
    low = talib.MIN(close, n)
    return high, low

# 定义MACD函数
def MACD(close, n_fast, n_slow, n_signal):
    macd, signal, hist = talib.MACD(close, n_fast, n_slow, n_signal)
    return macd, signal, hist

# 定义交易函数
def trade(open, close, high, low, macd, signal, hist):
    # 初始化持仓状态和交易信号
    position = 0
    signal = 0

    # 获取唐奇安通道上轨和下轨
    donchian_high, donchian_low = Donchian(close, 20)

    # 计算MACD和信号线
    macd, signal, hist = MACD(close, 12, 26, 9)

    # 判断交易信号
    if position == 0:
        # 如果当前没有持仓，判断开仓信号
        if macd[-1] > signal[-1] and hist[-1] > 0 and close[-1] > donchian_high[-1]:
            # 如果当前MACD大于信号线，且柱状图大于0，且收盘价高于唐奇安通道上轨，则开多仓
            signal = 1
        elif macd[-1] < signal[-1] and hist[-1] < 0 and close[-1] < donchian_low[-1]:
            # 如果当前MACD小于信号线，且柱状图小于0，且收盘价低于唐奇安通道下轨，则开空仓
            signal = -1
    else:
        # 如果当前有持仓，判断平仓信号
        if position > 0 and close[-1] < donchian_low[-1]:
            # 如果当前持多仓，且收盘价低于唐奇安通道下轨，则平多仓

模型四:机器学习策略

源码：

编写期货收益率预测模型的过程可能比较复杂，因为这类模型通常需要考虑许多因素。但是，以下是一个简单的Python程序，它使用Scikit-learn库来构建并训练一个期货收益率预测模型：

# Import necessary libraries
from sklearn import linear_model
from sklearn import metrics

# Load data
X = ... # Feature data
y = ... # Target data

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Train model
model = linear_model.LinearRegression()
model.fit(X_train, y_train)

# Evaluate model
y_pred = model.predict(X_test)
print(metrics.mean_squared_error(y_test, y_pred))

这个程序使用线性回归来构建模型，并用均方误差（MSE）来评估模型的性能。在实际应用中，您可能需要使用更复杂的模型，并使用多种指标来评估模型的性能。此外，您还需要准备足够的数据来训练模型，并确保数据的质量和准确性。

哈哈哈，太有意思了铁子们，我们和它聊聊天。

源码：

import matplotlib.pyplot as plt

# Set figure size
plt.figure(figsize=(4, 4))

# Draw squirrel
plt.plot([1, 1, 2, 2, 3, 3, 4], [1, 2, 2, 3, 3, 2, 2], 'k')
plt.plot([1.5, 1.5, 2.5, 2.5], [1, 2, 2, 1], 'k')
plt.plot([2, 2], [1.5, 2.5], 'k')

# Fill in the face and belly
plt.fill([1.5, 1.5, 2.5, 2.5], [1, 2, 2, 1], 'b')
plt.fill([2, 2], [1.5, 2.5], 'b')

# Draw the eyes
plt.plot([1.25, 1.75], [1.75, 1.75], 'k')
plt.plot([2.25, 2.75], [1.75, 1.75], 'k')

# Show the plot
plt.show()

哈哈哈哈，这也不像松鼠啊。

OK，还有没有更好玩的对话，评论区留言哈。

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