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algorithm.py
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algorithm.py
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# Written by Luke Graham
import math
import numpy as np
import pandas as pd
import statistics
from sklearn.linear_model import LinearRegression
# Custom trading Algorithm
class Algorithm():
########################################################
# NO EDITS REQUIRED TO THESE FUNCTIONS
########################################################
# FUNCTION TO SETUP ALGORITHM CLASS
def __init__(self, positions):
# Initialise data stores:
# Historical data of all instruments
self.data = {}
# Initialise position limits
self.positionLimits = {}
# Initialise the current day as 0
self.day = 0
# Initialise the current positions
self.positions = positions
# Initialise trade counters
self.uq_dollar_trades = 0
self.fun_drinks_trades = 0
self.coffee_trades = 0
self.beans_trades = 0
self.milk_trades = 0
self.fintech_token_trades = 0
self.red_pen_trades = 0
self.goober_eats_trades = 0
self.jean_trades = 0
# Helper function to fetch the current price of an instrument
def get_current_price(self, instrument):
# return most recent price
return self.data[instrument][-1]
########################################################
# RETURN DESIRED POSITIONS IN DICT FORM
def get_positions(self):
# Get current position
currentPositions = self.positions
# Get position limits
positionLimits = self.positionLimits
# Declare a store for desired positions
desiredPositions = {}
# Loop through all the instruments you can take positions on.
for instrument, positionLimit in positionLimits.items():
# For each instrument initilise desired position to zero
desiredPositions[instrument] = 0
# IMPLEMENT CODE HERE TO DECIDE WHAT POSITIONS YOU WANT
#######################################################################
def calculate_moving_average(data, day, window):
if day < window:
return None
price_window = data[day - window:day + 1]
moving_average = statistics.mean(price_window)
return moving_average
def calculate_volatility(data, period):
data = pd.Series(data)
price_changes = data.diff().abs()
volatility = price_changes.rolling(window=period, min_periods=1).std()
return volatility
def money_left_over(self):
total = 500000
for position in desiredPositions:
cost = abs(desiredPositions[position]) * self.data[position][self.day]
total -= cost
return total
#### Buy Fun Drinks (mean reversion) - $764,900.00 ####
savings = money_left_over(self)
qnty = savings / self.data["Fun Drink"][self.day]
prev_days = min(7, self.day)
one_day_ma = calculate_moving_average(self.data["Fun Drink"], self.day, 1)
seven_day_ma = calculate_moving_average(self.data["Fun Drink"], self.day, prev_days)
# ----diff variables for second moving average indicator----
prev_days2 = min(8, max(self.day, 5))
five_day_ma2 = calculate_moving_average(self.data["Fun Drink"], self.day, 5)
eight_day_ma2 = calculate_moving_average(self.data["Fun Drink"], self.day, prev_days2)
if one_day_ma == None or seven_day_ma == None:
desiredPositions["Fun Drink"] = min(math.floor(qnty), positionLimits["Fun Drink"])
self.fun_drinks_trades += 1
# ----Price above/below both moving averages----
elif self.data["Fun Drink"][self.day] < one_day_ma and one_day_ma < seven_day_ma:
desiredPositions["Fun Drink"] = min(math.floor(qnty), positionLimits["Fun Drink"])
self.fun_drinks_trades += 1
elif self.data["Fun Drink"][self.day] > one_day_ma and one_day_ma > seven_day_ma:
desiredPositions["Fun Drink"] = max(-(math.floor(qnty)), -(positionLimits["Fun Drink"]))
self.fun_drinks_trades += 1
# ----Price crosses moving averages----
elif eight_day_ma2 and self.data["Fun Drink"][self.day] > five_day_ma2 and five_day_ma2 < eight_day_ma2:
desiredPositions["Fun Drink"] = -(positionLimits["Fun Drink"])
self.fun_drinks_trades += 1
elif eight_day_ma2 and self.data["Fun Drink"][self.day] < five_day_ma2 and five_day_ma2 > eight_day_ma2:
desiredPositions["Fun Drink"] = positionLimits["Fun Drink"]
self.fun_drinks_trades += 1
#### Goober Eats (mean reversion) - $343,500.00 ####
savings = money_left_over(self)
qnty = savings / self.data["Goober Eats"][self.day]
prev_days = min(4, self.day)
one_day_ma = calculate_moving_average(self.data["Goober Eats"], self.day, 1)
four_day_ma = calculate_moving_average(self.data["Goober Eats"], self.day, prev_days)
# ----diff variables for second moving average indicator----
prev_days2 = min(4, max(self.day, 2))
two_day_ma2 = calculate_moving_average(self.data["Goober Eats"], self.day, 2)
four_day_ma2 = calculate_moving_average(self.data["Goober Eats"], self.day, prev_days2)
if one_day_ma == None or four_day_ma == None:
pass
# ----Price above/below both moving averages----
elif self.data["Goober Eats"][self.day] <= one_day_ma and one_day_ma < four_day_ma:
desiredPositions["Goober Eats"] = min(math.floor(qnty), positionLimits["Goober Eats"])
self.goober_eats_trades += 1
elif self.data["Goober Eats"][self.day] >= one_day_ma and one_day_ma > four_day_ma:
desiredPositions["Goober Eats"] = max(-(math.floor(qnty)), -(positionLimits["Goober Eats"]))
self.goober_eats_trades += 1
# ----Price crosses moving averages----
elif four_day_ma2 and self.data["Goober Eats"][self.day] > two_day_ma2 and two_day_ma2 < four_day_ma2:
desiredPositions["Goober Eats"] = max(-(math.floor(qnty)), -(positionLimits["Goober Eats"]))
self.goober_eats_trades += 1
elif four_day_ma2 and self.data["Goober Eats"][self.day] < two_day_ma2 and two_day_ma2 > four_day_ma2:
desiredPositions["Goober Eats"] = min(math.floor(qnty), positionLimits["Goober Eats"])
self.goober_eats_trades += 1
#### Buy Coffee (linear regression model) - $251,700.00 ####
if self.day < 1:
#pass
desiredPositions["Coffee"] = positionLimits["Coffee"]
self.coffee_trades += 1
else:
# Use past 25 days including today for features
prev_days = min(25, self.day)
milk_prices = self.data["Milk"][self.day - prev_days : self.day + 1]
bean_prices = self.data["Coffee Beans"][self.day - prev_days : self.day + 1]
coffee_prices = self.data["Coffee"][self.day - prev_days : self.day + 1]
# Prepare data for training (exluding todays price for training)
milk_array = np.array(milk_prices).reshape(-1, 1)
bean_array = np.array(bean_prices).reshape(-1, 1)
past_coffee_array = np.array(coffee_prices).reshape(-1, 1)
X = np.hstack((milk_array[:-1], bean_array[:-1], past_coffee_array[:-1]))
# Target prices (shifted 1 forward for next days price)
y = np.array(coffee_prices[1:])
# Model building
model = LinearRegression()
model.fit(X, y)
X_today = np.array([[self.data["Milk"][self.day], self.data["Coffee Beans"][self.day], self.data["Coffee"][self.day]]])
predicted_coffee_price = model.predict(X_today)
savings = money_left_over(self)
qnty = savings / self.data["Coffee"][self.day]
if predicted_coffee_price[0] > self.data["Coffee"][self.day]:
desiredPositions["Coffee"] = min(math.floor(qnty), positionLimits["Coffee"])
self.coffee_trades += 1
elif predicted_coffee_price[0] < self.data["Coffee"][self.day]:
desiredPositions["Coffee"] = max(-(math.floor(qnty)), -(positionLimits["Coffee"]))
self.coffee_trades += 1
#### Buy Thrifted Jeans (mean reversion) - $340,684.00 ####
savings = money_left_over(self)
qnty = savings / self.data["Thrifted Jeans"][self.day]
prev_days = min(8, self.day)
one_day_ma = calculate_moving_average(self.data["Thrifted Jeans"], self.day, 1)
eight_day_ma = calculate_moving_average(self.data["Thrifted Jeans"], self.day, prev_days)
if one_day_ma == None or eight_day_ma == None:
#pass
desiredPositions["Thrifted Jeans"] = min(math.floor(qnty), positionLimits["Thrifted Jeans"])
self.jean_trades += 1
# ----Price above/below both moving averages----
elif self.data["Thrifted Jeans"][self.day] < one_day_ma and one_day_ma < eight_day_ma:
desiredPositions["Thrifted Jeans"] = min(math.floor(qnty), positionLimits["Thrifted Jeans"])
self.jean_trades += 1
elif self.data["Thrifted Jeans"][self.day] > one_day_ma and one_day_ma > eight_day_ma:
desiredPositions["Thrifted Jeans"] = max(-(math.floor(qnty)), -(positionLimits["Thrifted Jeans"]))
self.jean_trades += 1
# ----Price crosses moving averages----
elif self.data["Thrifted Jeans"][self.day] > one_day_ma and one_day_ma < eight_day_ma:
desiredPositions["Thrifted Jeans"] = max(-(math.floor(qnty)), -(positionLimits["Thrifted Jeans"]))
self.jean_trades += 1
elif self.data["Thrifted Jeans"][self.day] < one_day_ma and one_day_ma > eight_day_ma:
desiredPositions["Thrifted Jeans"] = min(math.floor(qnty), positionLimits["Thrifted Jeans"])
self.jean_trades += 1
#### Buy Red pens if price is less (Volatility ependent stratergy) - $104,000.00 ####
savings = money_left_over(self)
qnty = savings / self.data["Red Pens"][self.day]
volatility = calculate_volatility(self.data["Red Pens"], 10)[self.day]
volatility_threshold = 0.014
if volatility and volatility > volatility_threshold:
prev_days = min(14, max(self.day, 3))
three_day_ma = calculate_moving_average(self.data["Red Pens"], self.day, 3)
fourteen_day_ma = calculate_moving_average(self.data["Red Pens"], self.day, prev_days)
if three_day_ma == None or fourteen_day_ma == None:
pass
elif self.data["Red Pens"][self.day] > three_day_ma * 0.98 and three_day_ma > fourteen_day_ma:
desiredPositions["Red Pens"] = min(math.floor(qnty), positionLimits["Red Pens"])
self.red_pen_trades += 1
elif self.data["Red Pens"][self.day] < three_day_ma * 1.02 and three_day_ma < fourteen_day_ma:
desiredPositions["Red Pens"] = max(-(math.floor(qnty)), -(positionLimits["Red Pens"]))
self.red_pen_trades += 1
else:
# Apply mean reversion strategy during low volatility
prev_days = min(10, max(self.day, 2))
two_day_ma = calculate_moving_average(self.data["Red Pens"], self.day, 2)
ten_day_ma = calculate_moving_average(self.data["Red Pens"], self.day, prev_days)
if two_day_ma == None or ten_day_ma == None:
pass
elif self.data["Red Pens"][self.day] < two_day_ma and two_day_ma < ten_day_ma:
desiredPositions["Red Pens"] = min(math.floor(qnty), positionLimits["Red Pens"])
self.red_pen_trades += 1
elif self.data["Red Pens"][self.day] > two_day_ma and two_day_ma > ten_day_ma:
desiredPositions["Red Pens"] = max(-(math.floor(qnty)), -(positionLimits["Red Pens"]))
self.red_pen_trades += 1
#### Buying Milk (ma) - $58,000.00 ####
savings = money_left_over(self)
qnty = savings / self.data["Milk"][self.day]
# prev_days = min(7, max(self.day, 2))
two_day_ma = calculate_moving_average(self.data["Milk"], self.day, 2)
seven_day_ma = calculate_moving_average(self.data["Milk"], self.day, 7)
if two_day_ma == None or seven_day_ma == None:
# pass
desiredPositions["Milk"] = min(math.floor(qnty), positionLimits["Milk"])
self.milk_trades += 1
# ----Price above/below both moving averages----
elif self.data["Milk"][self.day] <= two_day_ma and two_day_ma < seven_day_ma:
desiredPositions["Milk"] = min(math.floor(qnty), positionLimits["Milk"])
self.milk_trades += 1
elif self.data["Milk"][self.day] >= two_day_ma and two_day_ma > seven_day_ma:
desiredPositions["Milk"] = max(-(math.floor(qnty)), -(positionLimits["Milk"]))
self.milk_trades += 1
# ----Price crosses moving averages----
elif self.data["Milk"][self.day] > two_day_ma and two_day_ma < seven_day_ma:
desiredPositions["Milk"] = max(-(math.floor(qnty)), -(positionLimits["Milk"]))
self.milk_trades += 1
elif self.data["Milk"][self.day] < two_day_ma and two_day_ma > seven_day_ma:
desiredPositions["Milk"] = min(math.floor(qnty), positionLimits["Milk"])
self.milk_trades += 1
#### Buy Fintech Token (Volatility ependent stratergy) - $93,559.45 ####
savings = money_left_over(self)
qnty = savings / self.data["Fintech Token"][self.day]
volatility = calculate_volatility(self.data["Fintech Token"], 11)[self.day]
volatility_threshold = 12
if volatility and volatility > volatility_threshold:
# Apply momentum stratergy during high volitility
# prev_days = min(24, max(self.day, 10))
eight_day_ma = calculate_moving_average(self.data["Fintech Token"], self.day, 8)
tf_day_ma = calculate_moving_average(self.data["Fintech Token"], self.day, 24)
if eight_day_ma == None or tf_day_ma == None:
pass
# desiredPositions["Fintech Token"] = min(math.floor(qnty), positionLimits["Fintech Token"])
elif self.data["Fintech Token"][self.day] > eight_day_ma * 0.95 and eight_day_ma > tf_day_ma:
desiredPositions["Fintech Token"] = min(math.floor(qnty), positionLimits["Fintech Token"])
self.fintech_token_trades += 1
elif self.data["Fintech Token"][self.day] < eight_day_ma * 1.02 and eight_day_ma < tf_day_ma:
desiredPositions["Fintech Token"] = max(-(math.floor(qnty)), -(positionLimits["Fintech Token"]))
self.fintech_token_trades += 1
else:
# Apply mean reversion strategy during low volatility
prev_days = min(5, self.day)
one_day_ma = calculate_moving_average(self.data["Fintech Token"], self.day, 1)
five_day_ma = calculate_moving_average(self.data["Fintech Token"], self.day, prev_days)
if one_day_ma == None or five_day_ma == None:
# pass
desiredPositions["Fintech Token"] = min(math.floor(qnty), positionLimits["Fintech Token"])
self.fintech_token_trades += 1
elif self.data["Fintech Token"][self.day] < one_day_ma and one_day_ma < five_day_ma:
desiredPositions["Fintech Token"] = min(math.floor(qnty), positionLimits["Fintech Token"])
self.fintech_token_trades += 1
elif self.data["Fintech Token"][self.day] > one_day_ma and one_day_ma > five_day_ma:
desiredPositions["Fintech Token"] = max(-(math.floor(qnty)), -(positionLimits["Fintech Token"]))
self.fintech_token_trades += 1
#### Buying Coffee Beans (ma) - $36,843.42 ####
savings = money_left_over(self)
qnty = savings / self.data["Coffee Beans"][self.day]
prev_days = min(4, max(self.day, 1))
one_day_ma = calculate_moving_average(self.data["Coffee Beans"], self.day, 1)
four_day_ma = calculate_moving_average(self.data["Coffee Beans"], self.day, prev_days)
# ----diff variables for second moving average indicator----
prev_days2 = min(9, max(self.day, 2))
two_day_ma2 = calculate_moving_average(self.data["Coffee Beans"], self.day, 2)
nine_day_ma2 = calculate_moving_average(self.data["Coffee Beans"], self.day, prev_days2)
if one_day_ma == None or four_day_ma == None:
# pass
desiredPositions["Coffee Beans"] = min(math.floor(qnty), positionLimits["Coffee Beans"])
self.beans_trades += 1
# ----Price above/below both moving averages----
elif self.data["Coffee Beans"][self.day] < one_day_ma and one_day_ma < four_day_ma:
desiredPositions["Coffee Beans"] = min(math.floor(qnty), positionLimits["Coffee Beans"])
self.beans_trades += 1
elif self.data["Coffee Beans"][self.day] > one_day_ma and one_day_ma > four_day_ma:
desiredPositions["Coffee Beans"] = max(-(math.floor(qnty)), -(positionLimits["Coffee Beans"]))
self.beans_trades += 1
# ----Price crosses moving averages----
elif nine_day_ma2 and self.data["Coffee Beans"][self.day] > two_day_ma2 and two_day_ma2 < nine_day_ma2:
desiredPositions["Coffee Beans"] = max(-(math.floor(qnty)), -(positionLimits["Coffee Beans"]))
self.beans_trades += 1
elif nine_day_ma2 and self.data["Coffee Beans"][self.day] < two_day_ma2 and two_day_ma2 > nine_day_ma2:
desiredPositions["Coffee Beans"] = min(math.floor(qnty), positionLimits["Coffee Beans"])
self.beans_trades += 1
#### Buying UQ Dollar (scalping) - $66,174.77 ####
savings = money_left_over(self)
qnty = savings / self.data["UQ Dollar"][self.day]
if self.data["UQ Dollar"][self.day] < 100:
desiredPositions["UQ Dollar"] = min(math.floor(qnty), positionLimits["UQ Dollar"])
self.uq_dollar_trades += 1
elif self.data["UQ Dollar"][self.day] >= 100:
desiredPositions["UQ Dollar"] = max(-(math.floor(qnty)), -(positionLimits["UQ Dollar"]))
self.uq_dollar_trades += 1
if self.day == 364:
print("------------------------------------------")
print("This is the number of milk trades completed:" + str(self.milk_trades))
print("This is the number of beans trades completed:" + str(self.beans_trades))
print("This is the number of uq dollar trades completed:" + str(self.uq_dollar_trades))
print("This is the number of fintech trades completed:" + str(self.fintech_token_trades))
print("This is the number of fun drinks trades completed:" + str(self.fun_drinks_trades))
print("This is the number of coffee trades completed:" + str(self.coffee_trades))
print("This is the number of red pen trades completed:" + str(self.red_pen_trades))
print("This is the number of goober eats trades completed:" + str(self.goober_eats_trades))
print("This is the number of jeans trades completed:" + str(self.jean_trades))
print("------------------------------------------")
#######################################################################
# Return the desired positions
return desiredPositions