marcosan93

import random

def check_ace(hand): 
    """
    Checks if there's an ace in the hand in case total went over 21
    """
    if 'A' in hand:
        hand[hand.index('A')] = 'A.'
        return True
    else:

marcosan93

def dealer_turn(your_hand, dealer_hand, total, dtotal, r_count, true_cnt, deck, turn=True): 
    """
    Activates the dealer's turn if player's move was 'stay'
    """
    # Tallying wins, losses, and draws
    wins = 0
    draw = 0
    loss = 0
    
    # Looping through the moves

marcosan93

import pandas as pd

def card_counter(hand, strategy='Hi-Lo'):
    """
    Counting cards based on strategy selected
    Returns sum of the values
    """
    
    df = pd.read_pickle('Card_Counting_Values')

marcosan93

import pandas as pd

# Creating a simple dictionary to start the values
vals = {'2': 1, '3': 1, '4': 1, '5': 1, '6': 1, 
            '7': 0, '8': 0, '9': 0, '10': -1, 'J': -1, 
            'Q': -1, 'K': -1, 'A': -1, 'A.': -1}

# Converting to a DataFrame
df = pd.DataFrame(vals, index=[0])

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def player_move(your_hand, limit, true_cnt, dealer_hand):
    """
    Chooses 'hit' or 'stay' depending on the limit set and count
    """
    
    dtotal = hand_total(dealer_hand[:1])

    # Meaning there are plenty of face cards left
    if true_cnt > 0:
        if hand_total(your_hand) >= limit:

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# Declaring variables for use
results      = {}
winnings     = 0
total_games  = 0
games_sim    = 1000
rec_rounds   = 10
limit        = 16
strategies   = list(df.index)
random.shuffle(strategies) # shuffling the index for more randomization

marcosan93

import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.figure(figsize=(16,8))

# Plotting each strategies result
for i in results:
    plt.plot(games_sim, results[i], label=i+': '+str(results[i][-1])+'%')
    
plt.title("Blackjack Probability \nGraph 3")
plt.ylabel("Percentage Won (also Draws)")

marcosan93

# Reading in the data
bc = pd.read_csv('BTC-USD.csv')

# Converting to datetime
bc['Date'] = pd.to_datetime(bc.Date)

# Setting the index as the dates
bc.set_index('Date', inplace=True)

# Selecting only the dates from 2017-01-01 onwards

marcosan93

# Converting the data to a logarithmic scale
bc_log = pd.DataFrame(np.log(bc.Close))

# Differencing the log values
log_diff = bc_log.diff().dropna()

# Using the Dickey-Fuller test to check for stationarity
results = adfuller(log_diff.Close)
print(f"P-value: {results[1]}")

marcosan93

def best_param(model, data, pdq, pdqs):
    """
    Loops through each possible combo for pdq and pdqs
    Runs the model for each combo
    Retrieves the model with lowest AIC score
    """
    ans = []
    for comb in tqdm(pdq):
        for combs in tqdm(pdqs):
            try: