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- 🚀qmrExchange🚀
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.
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
Due to its precise resemblance to real-life trading venues, qmrExchange is perfectly suited for researching plenty of topics, such as:
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)
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).
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).
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 datetimeqmrExchange 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']sim = Simulator(from_date, to_date,time_interval)
sim.exchange.create_asset(tickers[0])- 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)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_15minOutput:
| 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 |
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()
In order to further explore the project, take a look at our documentation: 🗎 Documentation