🚀qmrExchange🚀

🚀qmrExchange🚀

qmrExchange Overview

The qmrExchange project is an open-source financial markets exchange simulator that realistically mimics all the main components of modern trading venues. It allows us to test and quantify the behavior of different agents in a laboratory and isolated environment without the high noise-to-signal ratio that is otherwise unavoidable in live settings. By creating a completely functioning trading venue whose access is only granted to a finite and known number of agents or trading algorithms, qmrExchange enables analyzing causation and quantifying the impact of each agent in a way that is otherwise unfeasible.

Use cases for qmrExchange

The implementation of qmrExchange closely resembles the backend of most FIFO trading exchanges and replicates the market microstructure of the most popular venues. As a consequence, the system is especially useful for:

Teaching, studying, and researching topics related to market microstructure and algorithmic trading.
Estimating the impact of new regulations and how they affect each type of agent
Implementing and analyzing market-making and high-frequency trading algorithms
Creating algorithmic trading challenges and tournaments both for university students and industry professionals alike

Potential Research Topics with qmrExchange

Due to its precise resemblance to real-life trading venues, qmrExchange is perfectly suited for researching plenty of topics, such as:

Market-Making Algorithms

By implementing a finite number of market participants, such as institutional investors and indicator-based trading algorithms, market-making algorithms can be studied. For a rigorous implementation, refer to Avellaneda & Stoikov (2008)

Optimal Execution Algorithms

qmrExchange is an ideal environment for implementing, testing, and quantifying the market impact of different execution algorithms. By creating a laboratory, sterile and isolated venue whose market participants and their behavior is known with absolute certainty, optimal execution algorithms can be easily implemented, researched, and calibrated. For a formal presentation of such an algorithm, refer to Almgren & Chriss (1999).

Adversarial Algorithms

Much like in the spirit of General Adversarial Networks and Game Theory, an implementation where a profit-maximizing agent’s behavior is calibrated based on the predefined behavior of other market participants is possible. For an interesting introduction to game theory applied to financial markets, refer to Allen & Morris (2022).

Basig usage of qmrExchange

Import required libraries

from source.qmr_exchange import Exchange
from random import Random
from source.qmr_exchange import Exchange, Simulator
from source.agents import RandomMarketTaker, NaiveMarketMaker
from datetime import datetime

Declare basic parameters for the simulation

qmrExchange allows for simulating multiple tickers at once, for statistical arbitrage and high-frequency-trading simulations. In the present case, we simulate 2 weeks worth of 1 minute data (24/7 trading).

from_date = datetime(2022,1,1)
to_date = datetime(2022,1,15)
time_interval = 'minute'
tickers = ['XYZ']

Instantiate a Simulator

sim = Simulator(from_date, to_date,time_interval)
sim.exchange.create_asset(tickers[0])

Add trading agents

We add a naive market maker that creates both buy and sell orders in each period. It quotes buy and sell prices based on the last traded price and the specified spread percentage.
We add a market taker that randomly buys and sells (based on the defined probabilities) on each period by means of market ordes (hence the word 'taker').

mm = NaiveMarketMaker(name='market_maker', tickers=tickers, aum=1_000, spread_pct=0.005, qty_per_order=4)
sim.add_agent(mm)

mt = RandomMarketTaker(name='market_taker', tickers=tickers, aum=1_000, prob_buy=.2, prob_sell=.2, qty_per_order=1,seed=42)
sim.add_agent(mt)

Run the simulation

sim.run()

Retrieve all executed trades of our simulation

sim.trades

| dt                  | ticker   |   qty |   price | buyer        | seller       |
|:--------------------|:---------|------:|--------:|:-------------|:-------------|
| 2022-01-01 00:00:00 | XYZ      |     1 |  100    | init_seed    | init_seed    |
| 2022-01-01 00:04:00 | XYZ      |     1 |  100.25 | market_taker | market_maker |
| 2022-01-01 00:10:00 | XYZ      |     1 |  100    | market_maker | market_taker |
| 2022-01-01 00:11:00 | XYZ      |     0 |   99.75 | market_maker | market_taker |
| 2022-01-01 00:13:00 | XYZ      |     0 |   99.5  | market_maker | market_taker |
| 2022-01-01 00:14:00 | XYZ      |     0 |   99.25 | market_maker | market_taker |
| 2022-01-01 00:15:00 | XYZ      |     1 |   99.5  | market_taker | market_maker |
| 2022-01-01 00:16:00 | XYZ      |     1 |   99.75 | market_taker | market_maker |
| 2022-01-01 00:18:00 | XYZ      |     2 |   99.5  | market_maker | market_taker |
| 2022-01-01 00:19:00 | XYZ      |     0 |   99.25 | market_maker | market_taker |
| 2022-01-01 00:20:00 | XYZ      |     0 |   99    | init_seed    | market_taker |
| 2022-01-01 00:21:00 | XYZ      |     1 |   99.25 | market_taker | market_maker |
| 2022-01-01 00:22:00 | XYZ      |     1 |   99    | init_seed    | market_taker |
| 2022-01-01 00:24:00 | XYZ      |     1 |   99.25 | market_taker | market_maker |
| 2022-01-01 00:25:00 | XYZ      |     1 |   99.5  | market_taker | market_maker |
| 2022-01-01 00:27:00 | XYZ      |     2 |   99.25 | market_maker | market_taker |
| 2022-01-01 00:28:00 | XYZ      |     1 |   99.5  | market_taker | market_maker |
| 2022-01-01 00:30:00 | XYZ      |     1 |   99.25 | market_maker | market_taker |
| 2022-01-01 00:38:00 | XYZ      |     0 |   99    | market_maker | market_taker |
| 2022-01-01 00:39:00 | XYZ      |     0 |   98.75 | market_maker | market_taker |

Group asset price in fixed 15 Minute OHLCV Bars

df_15min = sim.get_price_bars(ticker=tickers[0],bar_size='15Min')
df_15min

Output:

| dt                  |   open |   high |   low |   close |   volume |
|:--------------------|-------:|-------:|------:|--------:|---------:|
| 2022-01-01 00:00:00 | 100    | 100.25 | 99.25 |   99.25 |        3 |
| 2022-01-01 00:15:00 |  99.5  |  99.75 | 99    |   99.5  |       11 |
| 2022-01-01 00:30:00 |  99.25 |  99.25 | 98.5  |   98.75 |        2 |
| 2022-01-01 00:45:00 |  98.5  |  98.5  | 98    |   98.24 |        2 |
| 2022-01-01 01:00:00 |  97.99 |  98.73 | 97.99 |   98.23 |        9 |
| 2022-01-01 01:15:00 |  98.48 |  99.23 | 98.48 |   99.23 |       11 |
| 2022-01-01 01:30:00 |  99.48 | 100.23 | 99.48 |   99.73 |        9 |
| 2022-01-01 01:45:00 |  99.48 |  99.48 | 98.98 |   98.98 |        2 |
| 2022-01-01 02:00:00 |  99.23 |  99.73 | 98.73 |   99.73 |        9 |
| 2022-01-01 02:15:00 |  99.48 |  99.48 | 98.73 |   98.73 |        5 |
| 2022-01-01 02:30:00 |  98.98 |  99.73 | 98.98 |   99.23 |       10 |
| 2022-01-01 02:45:00 |  98.98 |  99.98 | 98.98 |   99.73 |        8 |
| 2022-01-01 03:00:00 |  99.98 |  99.98 | 99.73 |   99.73 |        4 |
| 2022-01-01 03:15:00 |  99.48 |  99.73 | 99.48 |   99.73 |        5 |
| 2022-01-01 03:30:00 |  99.48 |  99.73 | 98.98 |   99.73 |        4 |
| 2022-01-01 03:45:00 |  99.48 |  99.73 | 99.23 |   99.23 |        5 |
| 2022-01-01 04:00:00 |  99.48 |  99.73 | 99.23 |   99.73 |        9 |
| 2022-01-01 04:15:00 |  99.98 |  99.98 | 99.23 |   99.48 |        7 |
| 2022-01-01 04:30:00 |  99.23 |  99.73 | 98.98 |   99.73 |        5 |
| 2022-01-01 04:45:00 |  99.98 | 100.23 | 99.73 |   99.73 |        7 |

Retrieve a dataframe of an agents holding at each period of time

mt_holdings = sim.get_portfolio_history('market_taker')
mm_holdings = sim.get_portfolio_history('market_maker')

| dt                  |    XYZ |    cash |     aum |
|:--------------------|-------:|--------:|--------:|
| 2022-01-01 00:00:00 |   0    | 1000    | 1000    |
| 2022-01-01 00:01:00 |   0    | 1000    | 1000    |
| 2022-01-01 00:02:00 |   0    | 1000    | 1000    |
| 2022-01-01 00:03:00 |   0    | 1000    | 1000    |
| 2022-01-01 00:04:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:05:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:06:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:07:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:08:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:09:00 | 100.25 |  899.75 | 1000    |
| 2022-01-01 00:10:00 |   0    |  999.75 |  999.75 |
| 2022-01-01 00:11:00 |   0    |  999.75 |  999.75 |
| 2022-01-01 00:12:00 |   0    |  999.75 |  999.75 |
| 2022-01-01 00:13:00 |   0    |  999.75 |  999.75 |
| 2022-01-01 00:14:00 |   0    |  999.75 |  999.75 |
| 2022-01-01 00:15:00 |  99.5  |  900.25 |  999.75 |
| 2022-01-01 00:16:00 | 199.5  |  800.5  | 1000    |
| 2022-01-01 00:17:00 | 199.5  |  800.5  | 1000    |
| 2022-01-01 00:18:00 |   0    |  999.5  |  999.5  |
| 2022-01-01 00:19:00 |   0    |  999.5  |  999.5  |

Plot the results

Create a candlestick chart for the asset price.

from source.helpers import plot_bars
df_15min = sim.get_price_bars(ticker=tickers[0], bar_size='15Min')
plot_bars(df_15min)

Plot the assets under management of each agent

import plotly.express as px
import pandas as pd

df_plot = pd.DataFrame()
df_plot['Market Maker'] = mm_holdings['aum']
df_plot['Market Taker'] = mt_holdings['aum']
fig = px.line(df_plot,labels={'variable':'Agents','value':'Assets Under Management','dt':'Date'})
fig.show()

Project Documentation

In order to further explore the project, take a look at our documentation: 🗎 Documentation

welly87/qmrExchange