Scipy Optimize Maximize with a dataframe

Question

I have a df consisting of monthly share prices. I was hoping to find the optimal buy price and sell price to maximize earnings (revenue - costs). From research, it appears Scipy Optimize is the best tool to use, however all the examples I've seen do not show it being used with a dataframe.

A previous question sort of covered this. but I couldn't get it working for me, as my buy and sell quantities will change depending on the price. Meaning I need to recalc the df fields.

Any help would be greatly appreciated!

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']

hi @WeNYoBen, thanks for your reply. could you please elaborate a bit further? I am not sure how to incorpoate a for loop, Should I first create a function to add fields (Qty, Buy, Sell, Cost, Rev) to my df and returns -1 * (revenue - cost). then use that in my scipy minimize optimisation. — Bobby Heyer
– Bobby Heyer, Commented Oct 27, 2019 at 23:25

Bobby Heyer · Accepted Answer · 2019-10-31 02:15:11Z

So I ended up finding a way to use scipy optimize with my dataset.

I wrote a predicate which would allow me to solve my field calculations. then I called it in another function which I used for my scipy optimization.

This is not a very good optimization solution, as I don't seem to get very far from my initial guess. But it at least solved the question I had asked here.

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())

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