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simulation.py
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simulation.py
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# Written by UQ FinTech Club
# Library Imports
import os
import pandas as pd
from algorithm import Algorithm
import numpy as np
import matplotlib.pyplot as plt
from decimal import Decimal, ROUND_HALF_UP
from matplotlib.gridspec import GridSpec
import math
##############################
# Define constants
##############################
# PRODUCTS AND THEIR INDIVIDUAL BUDGETS IN $AUD
positionLimits = {
"Fintech Token": 35,
"Fun Drink": 10000,
"Red Pens": 40000,
"Thrifted Jeans": 400,
"UQ Dollar": 650,
"Coffee": 30000,
"Coffee Beans": 200,
"Goober Eats": 75000,
"Milk": 2500
}
# TOTAL DAILY BUDGET IN $AUD
totalDailyBudget = 500000
##############################
# Trading Engine Class, Controlling Trades Tracking
class TradingEngine:
# def __init__(self, dataFolder='./training_data/'):
def __init__(self, dataFolder='./competition_data/'):
# Init variables
self.dataFolder = dataFolder
# Store active positions
self.positions = {}
# Position Limits
self.positionLimits = positionLimits
# Daily return history of each instrument
self.returnsHistory = {}
# Instrument cumulative returns history
self.cumulativeReturnsHistory = {}
# Store position as % of limit for graphing
self.pcPositionHistorys = {}
# Daily return history of combined instruments
self.totalReturnHistory = []
# Cumulative historical value of combined instruments
self.totalValueHistory = []
# Total days in simulation
self.totalDays = 0
# Track total PNL across all instruments
self.totalPNL = 0
# Track pc of total budget used daily
self.pcTotalBudget = []
# Setup functions
self.load_data()
self.initialize_positions()
# For loading in relevant data from .CSV Files
def load_data(self):
self.data = {}
for file in os.listdir(self.dataFolder):
if file.endswith('_price_history.csv'):
instrumentName = file.split('_')[0]
if instrumentName in positionLimits.keys():
filePath = os.path.join(self.dataFolder, file)
self.data[instrumentName] = pd.read_csv(filePath)
else:
print(f"No position limit set for {instrumentName}. This dataset will not be loaded.")
# Ensure that all data have same length of days
numDays = len(self.data[list(self.data.keys())[0]])
for data in self.data.values():
# if a dataset has a different number of days, exit
if len(data) != numDays:
print("\nError, not all datasets are the same length.\bExiting...")
exit(1)
# Otherwise, all data should have the same number of days.
self.totalDays = numDays
print("Datasets loaded successfully.")
# Set initial positions to 0 for each
def initialize_positions(self):
for instrument in positionLimits:
self.positions[instrument] = 0
self.returnsHistory[instrument] = []
self.cumulativeReturnsHistory[instrument] = []
self.pcPositionHistorys[instrument] = []
# Helper function to check that a given order is within the daily budget.
# Also records the total utilisation of the daily budget.
def notWithinBudget(self, desiredPositions, priceHistory):
# calc total value of positions
totVal = 0
for instrument, history in priceHistory.items():
pos = desiredPositions[instrument]
price = history[-1]
value = abs(pos*price)
totVal += value
# if the budget is exceeded
if totVal > totalDailyBudget:
print("#########")
print(f"Over budget by ${totVal-totalDailyBudget}.")
# display values of each instrument position.
for instrument, prcHistory in priceHistory.items():
pos = desiredPositions[instrument]
price = prcHistory[-1]
value = abs(pos*price)
totVal += value
print(f"{instrument} position value: ${value}.")
print("#########")
# record zero as daily position used of budget, as position will be reset to zero
self.pcTotalBudget.append(0)
# return true, as desired position not within budget.
return True
# within daily budget, so record % usage
pcBudgetUsage = round(totVal*100/totalDailyBudget,2)
self.pcTotalBudget.append(pcBudgetUsage)
# return false, as within the daily budget
return False
# Process submitted algorithm
def run_algorithms(self, algorithmsInstance):
# Print position limits (added by Luke)
# for key, value in self.positionLimits.items():
# print(key, value)
# Loop through each day of data (leaving the last)
for day in range(self.totalDays):
# Get current data history at this point in time
historicalData = {}
for instrument, priceData in self.data.items():
# Fetch all relevant data
priceHistory = priceData['Price'][:day+1].tolist()
# Add them to the historicalData store
historicalData[instrument] = priceHistory
# Update the algorithms instance with the new information
algorithmsInstance.day = day
algorithmsInstance.data = historicalData
algorithmsInstance.positions = self.positions
algorithmsInstance.positionLimits = self.positionLimits
# Now get the desired positions from the competitors algorithm
desiredPositions = algorithmsInstance.get_positions()
# Check if the desired positions are within total budget
if self.notWithinBudget(desiredPositions, historicalData):
# Set all desired positions to zero
for instrument in desiredPositions.keys():
desiredPositions[instrument] = 0
print(f"REQUESTED POSIITONS EXCEED DAILY BUDGET OF: ${totalDailyBudget}.")
print(f"SET ALL DESIRED POSITIONS FOR DAY {day} TO ZERO.")
# Store total return for the day
dailyReturn = 0
# Process profit/loss for each instrument:
for instrument, priceData in self.data.items():
# Perform desired position quality checks (int and within limits)
if (type(desiredPositions[instrument]) != type(1) or # not an int
abs(desiredPositions[instrument]) > self.positionLimits[instrument] # not within limits
):
# Incorrect value provided. Skip
print(f"Position given for {instrument} on day {day} invalid.")
print(f"Position given was {desiredPositions[instrument]}.")
print(f"The limit for this instrument today is: {self.positionLimits[instrument]}")
print(f"Setting desired position to zero units.")
# Set zero
desiredPositions[instrument] = 0
# Calculate PNL if not day 0
if day != 0:
existingPosition = self.positions[instrument]
currPrice = priceData["Price"][day]
lastPrice = priceData["Price"][day-1]
instrumentPNL = existingPosition * (currPrice - lastPrice)
instrumentPNL = quantize_decimal(instrumentPNL, 2)
self.returnsHistory[instrument].append(instrumentPNL)
self.cumulativeReturnsHistory[instrument].append(
instrumentPNL + self.cumulativeReturnsHistory[instrument][-1]
)
# add it to the daily return
dailyReturn += instrumentPNL
else:
# No trades executed first day
self.returnsHistory[instrument].append(0)
self.cumulativeReturnsHistory[instrument].append(0)
# Store positions in historical tracker for graphing
for instrument, desiredPosition in desiredPositions.items():
self.pcPositionHistorys[instrument].append(round(desiredPosition*100/self.positionLimits[instrument]))
# Add the daily return to the tracker
self.totalReturnHistory.append(dailyReturn)
# Update total PNL
self.totalPNL += dailyReturn
# Display PNL
print(f"Total PNL @ Day {day}: {self.totalPNL}")
# Update total Value
self.totalValueHistory.append(self.totalPNL)
# Update simluator information
self.positions = desiredPositions
def plot_returns(self):
# Set figure size
fig = plt.figure(figsize=(16, 9))
gs = GridSpec(16, 16, figure=fig)
# Adjust spacing between subplots
fig.subplots_adjust(wspace=10, hspace=10)
ax1 = fig.add_subplot(gs[:8, 8:])
ax2 = fig.add_subplot(gs[8:12, 8:])
ax3 = fig.add_subplot(gs[:8, :8])
ax4 = fig.add_subplot(gs[12:, 8:])
ax5 = fig.add_subplot(gs[8:, :8])
# Print metrics
print('#' * 50)
print(f"Total PNL ($): {self.totalPNL}")
print('#' * 50)
for instrument, returns in self.cumulativeReturnsHistory.items():
instrumentReturn = returns[-1]
print(f"{instrument} Returns ($): {instrumentReturn}")
print('#' * 50)
# Store the lines for toggling visibility
lines = []
# Plot individual instrument returns
for instrument, returns in self.cumulativeReturnsHistory.items():
line, = ax1.plot(returns, label=instrument)
lines.append(line)
# Plot the point at the final value
#ax1.scatter(len(returns) - 1, returns[-1], color='red', zorder=5)
#ax1.annotate(f'{returns[-1]:.2f}', (len(returns) - 1, returns[-1]), textcoords="offset points", xytext=(0, 10), ha='center', bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.5'))
legend1 = ax1.legend()
ax1.set_title('Individual Instrument Cumulative P&L ($AUD)')
# Plot historical instrument positions
for instrument, returns in self.pcPositionHistorys.items():
line, = ax4.plot(returns, label=instrument, linewidth=1, alpha=1)
lines.append(line)
ax4.set_title(r'Historical Instrument Position (% of limit)')
# Plot total return history
line, = ax3.plot(self.totalValueHistory, label='Total Return', color='black')
lines.append(line)
# Plot a point at the final value of total return
ax3.scatter(len(self.totalValueHistory) - 1, self.totalValueHistory[-1], color='red', zorder=5)
ax3.annotate(f'{self.totalValueHistory[-1]:.2f}', (len(self.totalValueHistory) - 1, self.totalValueHistory[-1]), textcoords="offset points", xytext=(0, 10), ha='center', bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.5'))
legend3 = ax3.legend()
ax3.set_title('Cumulative Profit & Loss ($AUD)')
# Plot individual instrument historical P&L
for instrument, returns in self.returnsHistory.items():
line, = ax2.plot(returns, label=instrument)
lines.append(line)
ax2.set_title(r'Daily Individual P&L ($AUD)')
# Plot daily budget usage graph
line, = ax5.plot(self.pcTotalBudget, label='Budget Utilisation', color='black')
ax5.set_title('Percentage of Total Daily Limit Utilised (%)')
ax5.set_ylim(0, 100)
# Handle picking legend options
def on_pick(event):
legend_item = event.artist
is_visible = not legend_item.get_visible()
legend_item.set_visible(is_visible)
for orig_line in lines:
if legend_item.get_label() == orig_line.get_label():
orig_line.set_visible(is_visible)
fig.canvas.draw()
# Enable picking on legend items
for legend in [legend1, legend3]:
for legend_item in legend.get_lines():
legend_item.set_picker(True)
# Connect the pick event
fig.canvas.mpl_connect('pick_event', on_pick)
# Save the figure
output_dir = './simulation_results'
os.makedirs(output_dir, exist_ok=True)
fig.savefig(os.path.join(output_dir, 'returns_plot.png'), dpi=300)
plt.show()
plt.close(fig)
# Function to round a float
def quantize_decimal(value, decimal_places=2):
decimal_value = Decimal(value)
if decimal_value % 1 == 0: # Check if the number is an integer
return decimal_value.quantize(Decimal('1'), rounding=ROUND_HALF_UP)
else: # For non-integers, round to the specified decimal places
rounding_format = '1.' + '0' * decimal_places
return decimal_value.quantize(Decimal(rounding_format), rounding=ROUND_HALF_UP)
if __name__ == "__main__":
engine = TradingEngine()
algorithmInstance = Algorithm(engine.positions)
engine.run_algorithms(algorithmInstance)
engine.plot_returns()