sam-thecoder

def count_drop(numbers):
    return len([x for x in numbers if x < 0])

def count_up(numbers):
    return len([x for x in numbers if x > 0])

df.rename(columns={'Value': 'Value USD'}, inplace=True)
df['Value 2'] = df['Value USD'].shift(-1)
df['Max 7'] = df['Value USD'].rolling(7).max()
df['Min 7'] = df['Value USD'].rolling(7).min()

sam-thecoder

main_key = 0
game_record = {}
predictions = {
    2: {'loss': 0, 'profit': 0, 'draw': 0},
    1: {'loss': 0, 'profit': 0, 'draw': 0},
    0: {'loss': 0, 'profit': 0, 'draw': 0},
    -1: {'loss': 0, 'profit': 0, 'draw': 0},
    -2: {'loss': 0, 'profit': 0, 'draw': 0}
}
##Add Reward Cycle last

sam-thecoder

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

df = pd.read_csv('data/BCHAIN.csv')

df_copy = df.copy()

#Not needed for prediction

sam-thecoder

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import linear_model

df = pd.read_csv('data/BCHAIN.csv')

df_copy = df.copy()

#Not needed for prediction

sam-thecoder

def player_2(start_index=0, sleep=False, sleep_time=1, plot=False, output=True, miss_output=False, miss_plot=False, show_exception=True):
    user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(start_index=start_index)

    peak_price = day_info[1]
    min_price = day_info[4]
    max_price = day_info[5]
    mean_price = day_info[6]
    invest = None
    max_profit = invested

sam-thecoder

def lazy_investor(start_index=0, output=False, plot=True, amount=1000):
    user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(start_index=start_index)
    while game_active == False:
        if amount > 100:
            user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(user_key=user_key, invest_amount=amount-100)
            #print(bitcoin_game(user_key=user_key, invest_amount=amount-100))
        else:
            try:
                user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(user_key=user_key)
            except Exception as e:

sam-thecoder

def player_1(start_index=0, miss_output=False, miss_plot=False, sleep=False, sleep_time=1, plot=False, output=True, hold_max=1000, withdraw_max=500, withdraw_min=10):
    user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(start_index=start_index)
    prev_predict = day_info[-1]
    miss_colors = ['g']
    while game_active == False:
        #print(day_info[-1])
        if day_info[-1] == 0: #equilavent of 100 i.e. good odds
            #print('first option')
            if invested < 100: #invest 100
                user_key, day_info, amount, invested, profit, loss, game_active = bitcoin_game(user_key=user_key, invest_amount=withdraw_max/5)

sam-thecoder

main_key = 0
game_record = {}

##Add Reward Cycle last

def bitcoin_game(user_key=None, amount=1000, start_index=0, invest_amount=None, withdraw_amount=None):
    global main_key, game_record
    #print(user_key)
    if user_key == None:
        player_name = 'player_{0}'.format(main_key)

sam-thecoder

def count_drop(numbers):
    return len([x for x in numbers if x < 0])

def count_up(numbers):
    return len([x for x in numbers if x > 0])
  
df['Change'] = df['Value USD'] - df['Value 2']
df.drop('Value 2', axis=1, inplace=True)
#Change for the past 7 days
df['Mean Change 7'] = df['Change'].rolling(7).mean()

sam-thecoder

def decider(row):
    if row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] > 0 and row['Change'] > 0:
        return 0 #invest/hold or put in 100 investment if none
    #Withdraw 50%
    elif row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] > 0 and row['Change'] < 0:
        return -50
    #Withdraw 75%
    elif row['Drop 7'] < row['Up 7'] and row['Mean Change 7'] < 0 or row['Change'] < 0:
        return -75
    #invest 50% more