ref.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()
decadeAgo = today - timedelta(days=1095)

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

data = pdr.get_data_yahoo('BP.L', start=decadeAgo, end=today) 
print(data.shape)
print(data.Close[200])
# Other symbols: TSLA – Tesla, AMZN – Amazon, ZM – Zoom, ETH-USD – Ethereum-Dollar etc.

# Add two columns to this 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

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])
        
# convert data into dictionary of lists
# Store the above in a S3 bucket

'''
return redirect(/'results')


@app.route('/results')
def RandomHandler
	return doRender('auditpage.html, risk_values)
'''

# 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

# Lambda
minhistory = 3
shots = 10000
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)]

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