cameronShadmehry

import alpaca_trade_api as tradeapi

api = tradeapi.REST(
        'your_api_key_here',
        'https://paper-api.alpaca.markets', api_version='v2'
        
# Submit a market order to buy 1 share of Apple at market price
api.submit_order(
    symbol='AAPL',
    qty=1,

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# Convert the data into a dataframe.
df = pd.DataFrame(data=test['data'])

# Derived attributes from the data.
df['dollarAmount'] = df['change']*df['transactionPrice']
df['insiderPortfolioChange'] = df['change']/(df['share'] - df['change'])

# print(type(df['transactionPrice'][0]))
conditions = [
    (df['change'] >= 0) & (df['transactionPrice'] > 0),

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# Specify the stock that you want to analyze.
stock = 'TSLA'

# Request for data from Finhub.io (30 calls per second limit: https://finnhub.io/docs/api/rate-limit).
r = requests.get('https://finnhub.io/api/v1/stock/insider-transactions?symbol='+stock+'&token=YOUR API KEY')

# Load the JSON file as a string.
test = json.loads(r.text)

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# Necessary Libraries.
import requests
import json
import pandas as pd
import numpy as np

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mom_data = add_all_ta_features(hist_data, open="Open", high="High", low="Low", close="Close", volume="Volume") # Substantiate data with momentum indicators
mom_data.columns

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tick = yf.Ticker(str('MSFT'))
hist_data = tick.history(period="max")  # Tells yfinance what kind of data we want about this stock (In this example, all of the historical data)
hist_data.head() # Observe the historical stock data

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import yfinance as yf, pandas as pd
from ta import add_all_ta_features
from ta.utils import dropna

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i=0
while (i < len(tickers)) and (Amount_of_API_Calls < 1800):
    try:
        stock = tickers[i]  # Gets the current stock ticker
        temp = yf.Ticker(str(stock))  # Instantiate the ticker as a stock with Yahoo Finance
        Hist_data = temp.history(period="max")  # Tells yfinance what kind of data we want about this stock (In this example, all of the historical data)
        Hist_data.to_csv("C:\\Users\\Your Desired Path to File")  # Saves the historical data in csv format for further processing later
        time.sleep(2)  # Pauses the loop for two seconds so we don't cause issues with Yahoo Finance's backend operations
        Amount_of_API_Calls += 1 
        Stock_Failure = 0

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Hold_Results = []
list_files2 = (glob.glob("<Your Path>\\Bayesian_Logistic_Regression\\Stocks_Sub\\*.csv")) # Creates a list of all csv filenames in the stocks folder
for interval2 in list_files2:
    Stock_Name = ((os.path.basename(interval2)).split(".csv")[0])
    data = pd.read_csv(interval2,index_col=0)
    data = data.replace([np.inf, -np.inf], np.nan)
    data = data.fillna(0)
    dependents = [data["Five_Day_Observation_Outcome"].to_list(), data["Thirty_Day_Observation_Outcome"].to_list(), data["Sixty_Day_Observation_Outcome"].to_list()]
    data = data.drop(['Five_Day_Observation_Outcome', 'Thirty_Day_Observation_Outcome', 'Sixty_Day_Observation_Outcome', 'Date', 'Open', 'High', 'Low', 'Close'], axis = 1)
    scaler = StandardScaler()