samchaaa

from sklearn.qda import QDA
#from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing
import numpy as np
import pandas as pd

def initialize(context):
    context.assets = sid(8554) # Trade SPY
    context.model = QDA()
    context.lookback = 5 # Look back 

samchaaa

import alpaca_trade_api as tradeapi
import time
import datetime
from datetime import timedelta
from pytz import timezone
tz = timezone('EST')

api = tradeapi.REST('your key',
                    'your secret',
                    'https://paper-api.alpaca.markets')

samchaaa

import alpaca_trade_api as tradeapi
import time
import datetime
from datetime import timedelta
from pytz import timezone
tz = timezone('EST')

import numpy as np
import pandas as pd

samchaaa

import pandas as pd

oc = pd.DataFrame([line.strip('\n').split(',') for line in open('../../Downloads/quotedata.dat')])
oc.head()

oc.columns = oc.loc[2, :]
oc = oc.loc[3:]

oc['Calls K'] = oc.apply(lambda x: float(x['Calls'][-6:-2])/10, axis=1)
oc['Puts K'] = oc.apply(lambda x: float(x['Puts'][-6:-2])/10, axis=1)

samchaaa

def lookup(vin):
    m = getManu(vin)
    try:
        y = int(getYear(vin))
    except Exception as e:
        y = e
    # Exceptions here, or Sam may clean up in dict
    # if m!= None:
    #    if 'General Motors' in m:
    #        print(m)

samchaaa

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

samchaaa

def get_data(symbol, start, end):
    results = pd.DataFrame()
    while True:
        if start < end:
            # 15m bars
            d = api.polygon.historic_agg_v2(symbol, 15, 'minute', _from=start, to=end, unadjusted=False, limit=None).df
            time.sleep(.2)
            start = d.index[-1].strftime('%Y-%m-%d')
            results = pd.concat([results, d], axis=0)
        else:

samchaaa

if data_path not in os.listdir():
    os.mkdir(data_path)

# save as json
for ticker in tqdm(sp):    
    data = get_data(ticker, '2020-08-01', '2020-12-15')
    with open('./{}/{}.json'.format(data_path, ticker), 'w') as j:
        json.dump(data.to_json(), open('./{}/{}.json'.format(data_path, ticker), 'w'))

samchaaa

def hb_lb(ohlc, n, smoothing):
    
    """
    Creates a "high band" and "low band", based on the absolute pct movement for a given period. 
    This will trigger the mean reversion trades.
    
    'pct': percent change in the underlying, based on period n
    'abs_vol': the absolute exponential moving average of the pct movement
    
    """

samchaaa

def get_signals(close_time):
    
    """
    Creates signals for pybacktest.
    Note pybacktest specifically needs series named "buy", "sell", "short", "cover" to work.
    Check out tutorial notebook: https://nbviewer.jupyter.org/github/ematvey/pybacktest/blob/master/examples/tutorial.ipynb
    
    """

    bs = ohlc['C'] > ohlc['hb'] # sell signal