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initial commit of basic version
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‎AutoTrader.py

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from VirtualAccount import VirtualAccount
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import numpy as np
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from Config import *
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import time
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class AutoTrader:
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def __init__(self,model):
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self.advisor = model
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self.account = VirtualAccount()
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self.trade_amount = 100
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def buy(self):
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prev_bought_at = self.account.bought_btc_at # How much did I buy BTC for before
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if self.account.usd_balance - self.trade_amount >= 0:
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if prev_bought_at == 0 or self.account.last_transaction_was_sell or (prev_bought_at > self.account.btc_price): #or (self.account.btc_price/prev_bought_at -1 > 0.005):
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print(">> BUYING $",self.trade_amount," WORTH OF BITCOIN")
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self.account.btc_amount += self.trade_amount / self.account.btc_price
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self.account.usd_balance -= self.trade_amount
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self.account.bought_btc_at = self.account.btc_price
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self.account.last_transaction_was_sell = False
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else:
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print(">> Not worth buying more BTC at the moment")
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else:
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print(">> Not enough USD left in your account to buy BTC ")
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def sell(self):
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if self.account.btc_balance - self.trade_amount >= 0:
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if self.account.btc_price > self.account.bought_btc_at: # Is it profitable?
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print(">> SELLING $",self.trade_amount," WORTH OF BITCOIN")
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self.account.btc_amount -= (self.trade_amount / self.account.btc_price)
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self.account.usd_balance += self.trade_amount
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self.account.last_transaction_was_sell = True
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else:
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print(">> Declining sale: Not profitable to sell BTC")
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else:
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print(">> Not enough BTC left in your account to buy USD ")
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def runSimulation(self,samples,prices):
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print("> Trading Automatically for ",TESTING_MONTHS)
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day_count = 0
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for i in range(0,len(samples)):
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if i % 24 == 0:
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day_count += 1
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print("#################################################################################################")
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print("# Account Balance: $", (self.account.usd_balance + self.account.btc_balance), " BTC: $",
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self.account.btc_balance, " USD: $", self.account.usd_balance, "")
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print("#################################################################################################")
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print("########################################## DAY ",day_count," #########################################")
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if i % 6 == 0: # Perform a prediction every 6 hours
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prediction = self.advisor.predict(np.array([samples[i]]))
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#btc_price = samples[i][len(samples[i])-1]
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btc_price = prices[i]
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if self.account.btc_price != 0:
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self.account.btc_balance = self.account.btc_amount * btc_price
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self.account.btc_price = btc_price
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if prediction == 1:
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self.buy()
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else:
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self.sell()
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self.account.btc_balance = self.account.btc_amount * btc_price
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time.sleep(1) # Only for Visual Purposes
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print("#################################################################################################")
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print("# Account Balance: $", (self.account.usd_balance + self.account.btc_balance), " BTC: $",
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self.account.btc_balance, " USD: $", self.account.usd_balance, "")
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print("#################################################################################################")

‎CoinbaseAPI.py

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import cbpro
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import json
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import datetime
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import time
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class CoinbaseAPI:
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public_clinet = None
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def __init__(self):
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print("Coinbase API Initiated")
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self.public_client = cbpro.PublicClient()
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# [ time, low, high, open, close, volume ],
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def getCoinHistoricData(self,coin_pair,start,end,granularity):
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print("> Collecting historic data for "+coin_pair," from ",start," to ",end," every ",granularity," sec")
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data = []
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start_date = datetime.datetime.strptime(start, "%Y-%m-%d")
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end_date = datetime.datetime.strptime(end, "%Y-%m-%d")
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while (start_date <= end_date):
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print("> Date: ",start_date)
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start_limit = start_date
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end_limit = start_date + datetime.timedelta(hours=1)
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reversed_response = [] # Put in correct date order
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next_data = self.public_client.get_product_historic_rates(coin_pair, granularity=granularity,start=start_limit,end=end_limit)
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#print(self.public_client.get_products())
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for nd in next_data:
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reversed_response.append(nd)
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data.append(list(reversed(reversed_response)))
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start_date += datetime.timedelta(minutes=61)
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time.sleep(3)
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return data
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# [ time, low, high, open, close, volume ],
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"""
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time - bucket start time
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low - lowest price during the bucket interval
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high - highest price during the bucket interval
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open - opening price (first trade) in the bucket interval
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close - closing price (last trade) in the bucket interval
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volume - volume of trading activity during the bucket interval
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"""
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def getCoinCurrentData(self,coin_pair):
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data = self.public_client.get_product_historic_rates(coin_pair, granularity=60, start=datetime.datetime.today())
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return data

‎Config.py

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DATASET_DIR = "datasets/"
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COIN_PAIR = "BTC-USD"
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GRANULARITY = 60 # Data every 1 minute
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TRAINING_MONTHS = ["2018_06","2018_07","2018_08","2018_09","2018_10","2018_11","2018_12","2019_01",
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"2019_02","2019_03","2019_04","2019_05","2019_06","2019_07","2019_08","2019_09",
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"2019_10","2019_11","2019_12","2020_01","2020_02","2020_03","2020_04","2020_05",
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"2020_06","2020_07","2020_08","2020_09"]
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TESTING_MONTHS = ["2020_10"]
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# Model and Auto Trader
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CHANGE_RATE_THRESHOLD = 0.005
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TRAINING_WINDOW = 360 # Window to use for training in minutes
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LABELING_WINDOW = 360 # How far ahead to look for labeling / prediction

‎Controller.py

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from Dataset import Dataset
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from Model import Model
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from Config import *
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from AutoTrader import AutoTrader
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import argparse
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from CoinbaseAPI import CoinbaseAPI
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if __name__ == '__main__':
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print("\n\n\n ,.=ctE55ttt553tzs., \n",
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" ,,c5;z==!!:::: .::7:==it3>., \n",
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" ,xC;z!:::::: ::::::::::::!=c33x, \n",
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" ,czz!::::: ::;;..===:..::: ::::!ct3. \n",
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" ,C;/.:: : ;=c!:::::::::::::::.. !tt3. \n",
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" /z/.: :;z!:::::J :E3. E:::::::.. !ct3. \n",
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" ,E;F ::;t::::::::J :E3. E::. ::. \ztL \n",
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" ;E7. :c::::F****** **. *==c;.. :: Jttk \n",
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" .EJ. ;::::::L \:. ::. Jttl \n",
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" [:. :::::::::773. JE773zs. I:. ::::. It3L \n",
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" ;:[ L:::::::::::L |t::!::J |:::::::: :Et3 \n",
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" [:L !::::::::::::L |t::;z2F .Et:::.:::. ::[13 CRYPTO \n",
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" E:. !::::::::::::L =Et::::::::! ::|13 TRADING \n",
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" E:. (::::::::::::L ....... \:::::::! ::|i3 AI \n",
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" [:L !:::: ::L |3t::::!3. ]::::::. ::[13 \n",
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" !:( .::::: ::L |t::::::3L |:::::; ::::EE3 \n",
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" E3. :::::::::;z5. Jz;;;z=F. :E:::::.::::II3[ \n",
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" Jt1. :::::::[ ;z5::::;.::::;3t3 \n",
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" \z1.::::::::::l...... .. ;.=ct5::::::/.::::;Et3L \n",
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" \z3.:::::::::::::::J :E3. Et::::::::;!:::::;5E3L \n",
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" \cz\.:::::::::::::J E3. E:::::::z! ;Zz37` \n",
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" \z3. ::;:::::::::::::::;=' ./355F \n",
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" \z3x. ::~=======' ,c253F \n",
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" \ z3=. ..c5t32^ \n",
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" =zz3==... ...=t3z13P^ \n",
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" `*=zjzczIIII3zzztE3>*^` \n\n\n")
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print("\n> Welcome to Crypto-Trading AI")
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parser = argparse.ArgumentParser()
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parser.add_argument("--collect_coins",action="store_true")
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parser.add_argument("--train_and_trade", action="store_true")
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parser.add_argument("--start")
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parser.add_argument("--end")
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args = parser.parse_args()
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dataset = Dataset()
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if args.collect_coins:
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start_date = args.start if args.start else "2020-01-01"
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end_date = args.end if args.end else "2020-02-02"
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tokens = start_date.split("-")
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month = tokens[0] + "_" + tokens[1] + "_"
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coinbaseAPI = CoinbaseAPI()
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historic_data = coinbaseAPI.getCoinHistoricData(COIN_PAIR, start=start_date, end=end_date,granularity=GRANULARITY)
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dataset.storeRawCoinHistoricData(month,COIN_PAIR,historic_data)
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print("> Using Coinbase API to build dataset for ",COIN_PAIR)
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elif args.train_and_trade:
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print("> Creating Training Data for ", COIN_PAIR)
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data = dataset.loadCoinData(COIN_PAIR, TRAINING_MONTHS)
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x_train, y_train, _ = dataset.createTrainTestSets(COIN_PAIR, data, training_window=TRAINING_WINDOW, labeling_window=LABELING_WINDOW)
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print("> Creating Testing Data for ", COIN_PAIR)
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data = dataset.loadCoinData(COIN_PAIR, TESTING_MONTHS)
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x_test, y_test, prices = dataset.createTrainTestSets(COIN_PAIR, data, training_window=TRAINING_WINDOW, labeling_window=LABELING_WINDOW)
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test_model = Model("AutoTraderAI", x_train)
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test_model.train(x_train, y_train, batch_size=64, epochs=10)
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# test_model.evaluate(x_test,y_test)
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auto_trader = AutoTrader(test_model)
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auto_trader.runSimulation(x_test, prices)
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else:
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print("> The biggest mistake you can make in life is to waste your time. – Jerry Bruckner")
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print("> P.S. Use an argument next time: --collect_coins or --train_and_trade")
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