Scipy Optimize 使用数据框最大化

Question

我有一个包含每月股价的 df。我希望找到最佳的买入价和卖出价以最大化收益（收入 - 成本）。从研究来看，Scipy Optimize 似乎是最好的工具，但是我看到的所有示例都没有显示它与数据框一起使用。

以前的question 涵盖了这一点。但我无法让它为我工作，因为我的买卖数量会根据价格而变化。这意味着我需要重新计算 df 字段。

任何帮助将不胜感激！

import pandas as pd
import numpy as np
import math
import datetime
from scipy.optimize import minimize

df = pd.DataFrame({
    'Time': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'Price': [44, 100, 40, 110, 77, 109, 65, 93, 89, 73]})

# Create Empty Columns
df[['Qty', 'Buy', 'Sell', 'Cost', 'Rev']] = pd.DataFrame([[0.00, 0.00, 0.00, 0.00, 0.00]], index=df.index)

# Initial Values
buy_price = 50
sell_price = 100

# Set Values at Time 0
df.at[0, 'Qty'] = 0
df.at[0, 'Buy'] = np.where(df.at[0, 'Price'] < buy_price, min(30 - df.at[0, 'Qty'], 10), 0)
df.at[0, 'Sell'] = np.where(df.at[0, 'Price'] > sell_price, min(df.at[0, 'Qty'], 10), 0)
df.at[0, 'Cost'] = df.at[0, 'Buy'] * df.at[0, 'Price']
df.at[0, 'Rev'] = df.at[0, 'Sell'] * df.at[0, 'Price']

# Set Remaining Values
for t in range(1, len(df)):
    df.at[t, 'Qty'] = df.at[t-1, 'Qty'] + df.at[t-1, 'Buy'] - df.at[t-1, 'Sell']
    df.at[t, 'Buy'] = np.where(df.at[t, 'Price'] < buy_price, min(30 - df.at[t, 'Qty'], 10), 0)
    df.at[t, 'Sell'] = np.where(df.at[t, 'Price'] > sell_price, min(df.at[t, 'Qty'], 10), 0)
    df.at[t, 'Cost'] = df.at[t, 'Buy'] * df.at[t, 'Price']
    df.at[t, 'Rev'] = df.at[t, 'Sell'] * df.at[t, 'Price']

Answer 1

所以我最终找到了一种方法来对我的数据集使用 scipy 优化。

我写了一个谓词，可以让我解决我的字段计算。然后我在另一个用于我的 scipy 优化的函数中调用它。

这不是一个很好的优化解决方案，因为我似乎与我最初的猜测相差不远。但它至少解决了我在这里提出的问题。

import pandas as pd
import numpy as np
import scipy.optimize as optimize

df = pd.DataFrame({
    'Time': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'Price': [44, 100, 40, 110, 77, 109, 65, 93, 89, 49]})

# Create Empty Columns
df[['Qty', 'Buy', 'Sell', 'Cost', 'Rev']] = pd.DataFrame([[0.00, 0.00, 0.00, 0.00, 0.00]], index=df.index)


class Predicate:
    def __init__(self):
        self.prev_time = -1
        self.prev_qty = 0
        self.prev_buy = 0
        self.prev_sell = 0
        self.Qty = 0
        self.Buy = 0
        self.Sell = 0
        self.Cost = 0
        self.Rev = 0

    def __call__(self, x):
        if x.Time == self.prev_time: # apply runs first row/column twice, hence this
            x.Qty = self.prev_qty
            x.Buy = self.prev_buy
            x.Sell = self.prev_sell
            x.Cost = x.Buy * x.Price
            x.Rev = x.Sell * x.Price
        else:
            x.Qty = self.prev_qty + self.prev_buy - self.prev_sell
            x.Buy = np.where(x.Price < buy_price, min(30 - x.Qty, 10), 0)
            x.Sell = np.where(x.Price > sell_price, min(x.Qty, 10), 0)
            x.Cost = x.Buy * x.Price
            x.Rev = x.Sell * x.Price
            self.prev_buy = x.Buy
            self.prev_qty = x.Qty
            self.prev_sell = x.Sell
            self.prev_time = x.Time
        return x


# Define function to minimize
def rev(params):
    global buy_price
    global sell_price
    buy_price, sell_price = params
    df2 = df.apply(Predicate(), axis=1)
    return -1 * (df2['Rev'].sum() - df2['Cost'].sum())


# Run optimization
initial_guess = [40, 90]
result = optimize.basinhopping(func=rev, x0=initial_guess, niter=1000, stepsize=10)
print(result.x)

# Run the final results
result.x = buy_price, sell_price
df = df.apply(Predicate(), axis=1)
print(df)
print(df['Rev'].sum() - df['Cost'].sum())