test.py

#!/usr/bin/env python3

import math
import random
import yfinance as yf
import pandas as pd
from datetime import date, timedelta
from pandas_datareader import data as pdr

# override yfinance with pandas – seems to be a common step
yf.pdr_override()

# Get stock data from Yahoo Finance – here, asking for about 3 years
today = date.today()
timePast = today - timedelta(days=101)

# Get stock data from Yahoo Finance – here, Gamestop which had an interesting
# time in 2021: https://en.wikipedia.org/wiki/GameStop_short_squeeze

# Use the symbol you have been given in place of GME – e.g. AMZN.
data = pdr.get_data_yahoo("NVDA", start=timePast, end=today)
print(data)

# Add two columns to allow for Buy and Sell signals
# fill with zero
data["Buy"] = 0
data["Sell"] = 0

# Find the signals – uncomment print statements if you want to
# look at the data these pick out in some another way
# e.g. check that the date given is the end of the pattern claimed
print(len(data))
for i in range(2, len(data)):

    body = 0.01

    # Three Soldiers
    if (
        (data.Close[i] - data.Open[i]) >= body
        and data.Close[i] > data.Close[i - 1]
        and (data.Close[i - 1] - data.Open[i - 1]) >= body
        and data.Close[i - 1] > data.Close[i - 2]
        and (data.Close[i - 2] - data.Open[i - 2]) >= body
    ):
        data.at[data.index[i], "Buy"] = 1
        # print("Buy at ", data.index[i])

    # Three Crows
    if (
        (data.Open[i] - data.Close[i]) >= body
        and data.Close[i] < data.Close[i - 1]
        and (data.Open[i - 1] - data.Close[i - 1]) >= body
        and data.Close[i - 1] < data.Close[i - 2]
        and (data.Open[i - 2] - data.Close[i - 2]) >= body
    ):
        data.at[data.index[i], "Sell"] = 1
        # print("Sell at ", data.index[i])

# print(data)
# Data now contains signals, so we can pick signals with a minimum amount
# of historic data, and use shots for the amount of simulated values
# to be generated based on the mean and standard deviation of the recent history

minhistory = 7
shots = 10000
count_buy = 0
count_sell = 0
for i in range(minhistory, len(data)):
    # if data.Buy[i] == 1:  # if we’re interested in Buy signals
    #     mean = data.Close[i - minhistory : i].pct_change(1).mean()
    #     std = data.Close[i - minhistory : i].pct_change(1).std()
    #     # generate much larger random number series with same broad characteristics
    #     simulated = [random.gauss(mean, std) for x in range(shots)]
    #     # sort and pick 95% and 99%  - not distinguishing long/short risks here
    #     simulated.sort(reverse=True)
    #     var95 = simulated[int(len(simulated) * 0.95)]
    #     var99 = simulated[int(len(simulated) * 0.99)]
    #     count_buy += 1

    if data.Sell[i] == 1:  # if we’re interested in Buy signals
        mean = data.Close[i - minhistory : i].pct_change(1).mean()
        std = data.Close[i - minhistory : i].pct_change(1).std()
        # generate much larger random number series with same broad characteristics
        simulated = [random.gauss(mean, std) for x in range(shots)]
        # sort and pick 95% and 99%  - not distinguishing long/short risks here
        simulated.sort(reverse=True)
        var95 = simulated[int(len(simulated) * 0.95)]
        var99 = simulated[int(len(simulated) * 0.99)]
        count_sell += 1

        print(count_sell, var95, var99)  # only so you can see what is being produced