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arima_integration.py
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arima_integration.py
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# -*- coding: utf-8 -*-
"""ARIMA Integration.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1rQNl2oZWgQa5rn8l0m2J2IGP5u9BmeJe
"""
import os
import uuid
import numpy as np
import random
import requests
import time
import pandas as pd
import matplotlib.pyplot as plt
from enum import Enum
from datetime import date, timedelta
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_absolute_error, mean_squared_error
import IPython
from IPython.display import display, HTML, Javascript
# Constants
INITIAL_BALANCE = 1000000 # $1 million
INTERVALS = 1 # seconds
MINIMUM_GROWTH = 0.02 # minimum 2% increase in predicted price to trigger buy
STOP_LOSS_PERCENTAGE = 0.05 # limit loss to 5%
GOAL = 1200000 # $1.2 million
""" ARIMA Prediction Functions """
def preprocess_data(data_url):
df = pd.read_csv(data_url)
df['Date'] = pd.to_datetime(df['Date'])
data = df[['Date', 'Close']]
data = data.rename(columns={'Date': 'date', 'Close': 'price'})
data = data.set_index('date')
# Check if data is stationary
test_stationarity(data['price'])
return data
def train_arima_model(df_train):
m = ARIMA(df_train, order=(4,2,1))
m = m.fit()
return m
def predict_future_price(model, df_train, df_test, i):
forecast = model.forecast(steps=len(df_test))
return forecast.values[i]
def display_forecast(test_data, forecast):
# Plot the actual and forecasted Bitcoin prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, test_data['price'], label='Actual Price')
plt.plot(test_data.index, forecast, label='Forecasted Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Actual vs Forecasted Bitcoin Price')
plt.legend()
plt.show()
def test_stationarity(x):
#Determing rolling statistics
rolmean = x.rolling(window=22,center=False).mean()
rolstd = x.rolling(window=12,center=False).std()
#Plot rolling statistics:
orig = plt.plot(x, color='blue',label='Original')
mean = plt.plot(rolmean, color='red', label='Rolling Mean')
std = plt.plot(rolstd, color='black', label = 'Rolling Std')
plt.legend(loc='best')
plt.title('Rolling Mean & Standard Deviation')
plt.show(block=False)
#Perform Dickey Fuller test
result=adfuller(x)
print('ADF Stastistic: %f'%result[0])
print('p-value: %f'%result[1])
pvalue=result[1]
for key,value in result[4].items():
if result[0]>value:
print("The graph is non stationery")
break
else:
print("The graph is stationery")
break;
print('Critical values:')
for key,value in result[4].items():
print('\t%s: %.3f ' % (key, value))
class BitcoinTransaction:
def __init__(
self,
transaction_type,
price,
amount,
volume,
profit_or_loss=None,
transaction_trigger=None,
):
self.transaction_type = transaction_type
self.price = price
self.amount = amount
self.volume = volume
self.profit_or_loss = profit_or_loss
self.transaction_trigger = transaction_trigger
self.transaction_id = uuid.uuid4()
def __str__(self):
return f"Transaction ID: {self.transaction_id}, Transaction Type: {self.transaction_type}, Price: {self.price}, Amount: {self.amount} BTC, Volume: {self.volume}, Profit/Loss: {self.profit_or_loss}, Transaction Trigger: {self.transaction_trigger}"
def __repr__(self):
return f"({str(self)})"
class TransactionTypes(Enum):
BUY = 1
SELL = 2
""" Trading Bot Functions """
def get_price():
url = "https://pro-api.coinmarketcap.com/v1/cryptocurrency/quotes/latest"
headers = {"X-CMC_PRO_API_KEY": "f16d5846-68ea-4cbc-88c2-b2b0ae91ae25"}
params = {"symbol": "BTC"}
response = requests.get(url, headers=headers, params=params)
if response.status_code == 200:
data = response.json()
return data["data"]["BTC"]["quote"]["USD"]["price"]
else:
return None
def take_decision(current_price, predicted_price, balance, buy_order, sell_order):
# no buy orders placed yet
if buy_order is None:
# Check if price is predicted to grow beyond minimum required growth
if predicted_price >= current_price * (1 + MINIMUM_GROWTH):
# Check if we have sufficient balance
if balance > 0:
# Calculate the volume of BTC we can buy with our balance and the dollar amount
volume = balance / current_price
amount = balance
# Place the buy order
trigger = "Predicted future growth"
buy_order = BitcoinTransaction(
TransactionTypes.BUY, current_price, amount, volume, trigger
)
print("New buy order placed:", buy_order)
balance -= amount
else:
print("Insufficient balance to place a new buy order")
else:
print(
f"Predicted future price does not meet the minimum growth requirement ({MINIMUM_GROWTH * 100}%) for buy trigger"
)
elif sell_order is None:
# Check if current price has fallen to trigger stoploss sell, minimize loss
if current_price <= buy_order.price * (1 - STOP_LOSS_PERCENTAGE):
volume = buy_order.volume
amount = current_price * volume
# calculate the loss (negative profit) incurred in this sell order
profit_or_loss = volume * (current_price - buy_order.price)
trigger = "Current price triggered stoploss"
sell_order = BitcoinTransaction(
TransactionTypes.SELL,
current_price,
amount,
volume,
profit_or_loss,
trigger,
)
print("New sell order placed:", sell_order)
balance += amount
# Check if investment goal has been reached
elif balance + (current_price * buy_order.volume) >= GOAL:
volume = buy_order.volume
amount = current_price * volume
# calculate the profit/loss incurred in this sell order
profit_or_loss = volume * (current_price - buy_order.price)
trigger = "Investment goal reached"
sell_order = BitcoinTransaction(
TransactionTypes.SELL,
current_price,
amount,
volume,
profit_or_loss,
trigger,
)
print("New sell order placed:", sell_order)
balance += amount
# Check if predicted future price will fall below stoploss of current price, prevent possible loss
elif predicted_price <= current_price * (1 - STOP_LOSS_PERCENTAGE):
volume = buy_order.volume
amount = current_price * volume
# calculate the profit/loss incurred in this sell order
profit_or_loss = volume * (current_price - buy_order.price)
trigger = "Predicted future price triggered stoploss"
sell_order = BitcoinTransaction(
TransactionTypes.SELL,
current_price,
amount,
volume,
profit_or_loss,
trigger,
)
print("New sell order placed:", sell_order)
balance += amount
else:
print("Waiting for price to reach sell threshold")
return buy_order, sell_order, balance
def configure_browser_state():
display(
IPython.core.display.HTML(
"""
<canvas id="myChart"></canvas>
"""
)
)
display(
IPython.core.display.HTML(
"""
<script src="https://cdn.jsdelivr.net/npm/[email protected]"></script>
<script>
var ctx = document.getElementById('myChart').getContext('2d');
var chart = new Chart(ctx, {
// The type of chart we want to create
type: 'line',
// The data for our dataset
data: {
labels: [getDateTime(-10), getDateTime(-20), getDateTime(-30),
getDateTime(-40), getDateTime(-50), getDateTime(-60) ],
datasets: [{
label: 'Actual',
borderColor: 'rgb(255, 99, 132)',
data: [0,1,2,3,4,5]
},
{
label: 'Predicted',
borderColor: 'rgb(155, 199, 32)',
data: [0,1,2,3,4,5]
}]
},
// Configuration options go here
options: { animation: {duration: 0} ,
scales: {x: {
type: 'time',
time: { unit: 'minute',displayFormats: {minute: 'HH:mm'},tooltipFormat: 'HH:mm'},
title: {display: true, text: 'Date'}},
y: {
title: { display: true, text: 'value'}},
xAxes: [{ scaleLabel: { display: true, labelString: 'Timestamp [YYYY-MM-DD hh:mm:ss]'}}],
yAxes: [{scaleLabel: {display: true, labelString: 'BitCoin Price [USD $]'} }], },
title: { display: true, text: 'Bitcoin Price - Realtime Prediction'}}});
function getEpoch(offset_sec=0) {
var now = new Date();
return Math.floor((now.getTime() - offset_sec*1000)/1000);}
function getDateTime(offset_sec=0) {
var now = new Date();
var numberOfMlSeconds = now.getTime() - offset_sec*1000;
var update_now = new Date (numberOfMlSeconds);
var year = update_now.getFullYear();
var month = update_now.getMonth()+1;
var day = update_now.getDate();
var hour = update_now.getHours();
var minute = update_now.getMinutes();
var second = update_now.getSeconds();
if(month.toString().length == 1) {
month = '0'+month;}
if(day.toString().length == 1) {
day = '0'+day;}
if(hour.toString().length == 1) {
hour = '0'+hour;}
if(minute.toString().length == 1) {
minute = '0'+minute; }
if(second.toString().length == 1) {
second = '0'+second;}
var dateTime = year+'-'+month+'-'+day+' '+hour+':'+minute+':'+second;
return dateTime;
}
function addData(value, value2){
chart.data.labels.push(getDateTime())
chart.data.datasets[0].data.push(value)
chart.data.datasets[1].data.push(value2)
// optional windowing
if(chart.data.labels.length > 100) {
chart.data.labels.shift()
chart.data.datasets[0].data.shift()
chart.data.datasets[1].data.shift() }
chart.update();
}
</script>
"""
)
)
# Main program
def main():
balance = INITIAL_BALANCE
buy_order = None
sell_order = None
goal_reached = False
# Ready graph display
configure_browser_state()
# url = 'https://raw.githubusercontent.com/yetanotherpassword/COMS4507/main/BTC-USD.csv'
# update this url to new dataset for future retraining.
url = 'https://raw.githubusercontent.com/yetanotherpassword/COMS4507/main/BTC-USD.csv'
preprocessed_data = preprocess_data(url)
last_day_index = len(preprocessed_data.index)
# Split the data into train and test sets
#All data except approximately last three months, i.e. 100 datapoints are used to train the model.
df_train = preprocessed_data.iloc[:-100]
df_test = preprocessed_data.iloc[-100:]
model = train_arima_model(df_train)
transaction_record = []
profit_and_loss_record = []
# FIXME the line below is for experimental demonstration, remove line to get real prices
price = get_price()
test_prices = df_test['price']
i = 0
# keep running the bot if there is positive balance or an existing buy order has been placed.
while (balance > 0 or buy_order) and not goal_reached:
# FIXME
# uncomment the following line to get real prices
# current_price = get_price()
# FIXME
# the following line is for experimental demonstration, remove line to get real prices
if i > len(test_prices):
i = len(test_prices)
current_price = test_prices[i]
if not current_price:
current_price = random.randint(int(price * (1 - 0.1)), int(price * (1 + 0.1)))
if current_price is not None:
print("Current price of BTC: $", current_price)
predicted_price = predict_future_price(model, df_train, df_test, i)
print("PREDICTED PRICE $", predicted_price)
display(
Javascript(
"addData(" + str(current_price) + "," + str(predicted_price) + ")"
)
)
print("Predicted future price of BTC: $", predicted_price)
buy_order, sell_order, balance = take_decision(
current_price, predicted_price, balance, buy_order, sell_order
)
if sell_order:
# record transaction
transaction_record.append(sell_order)
print("Sell order fulfilled. Profit: $", sell_order.profit_or_loss)
profit_and_loss_record.append(
{sell_order.transaction_id: sell_order.profit_or_loss}
)
# reset orders
buy_order = None
sell_order = None
elif buy_order:
# record transaction
transaction_record.append(buy_order)
if balance >= GOAL:
goal_reached = True
print("Investment goal reached! Stop trading.")
print("Remaining balance (USD): $", balance, "\n")
else:
print("Error getting price from CoinMarketCap API")
time.sleep(INTERVALS)
i += 1
print("Transaction record:", transaction_record)
print("Profit and Loss:", profit_and_loss_record)
if __name__ == "__main__":
main()