FINANCIAL ENGINEERING CLUB
Algorithmic Trading
Financial Engineering Club
Definition
• Algorithmic trading, also called automated trading,
black-box trading, or algo-trading, is the use of
electronic platforms for entering trading orders with
an algorithm which executes pre-programmed
trading instructions whose variables may include
timing, price, or quantity of the order, or in many
cases initiating the order by automated computer
programs.
Motivation
• Algorithms can process larger amounts of data than humans.
• Algorithms can make computations and decisions faster than humans.
• Algorithms can execute more precisely.
• A simple strategy can be automated so that people can focus their time
elsewhere.
Objectives
• Smart Execution
• Automating a Strategy
Objectives
Deals with the execution of
an order.
• Smart Execution
••Smart
SmartExecution
Execution
•
Used by large brokers, asset
managers, etc. when placing orders
•
How can I place a large order and not
get screwed?
• Automating a Strategy
••Automating
Automatingaa Strategy
Strategy
How To Not Get Screwed
• When placing a large order, try to minimize your impact on the market.
• Scenario: I want to buy 50,000 shares of Chipotle (CMG) for $33,204,500:
a) Put in order for all 50,000 at once
b) Break order into 500 100-share lots and post all
c) Break order in 500 100-share lots and post over one hour,
considering how the market reacts
How To Not Get Screwed
• When placing a large order, try to minimize your impact on the market.
• Scenario: I want to buy 50,000 shares of Chipotle (CMG) for $33,204,500:
a) Put in order for all 50,000 at once
b) Break order into 500 100-share lots and post all
c) Break order in 500 100-share lots and post over one hour,
considering how the market reacts
How To Not Get Screwed
• Impact Driven Algorithms
• Reduce the effect that trading has on an asset’s price
• Iceberging – split larger order into many smaller ones:
• TWAP – Time Weighted Average Price
• VWAP – Volume Weighted Average Price
• More dynamic derivations
• Stops others from knowing you placed a large order
and changing their positions, costing you money!
Time Weighted Average Price
• Attempt to match the benchmark of how an asset price
changes over time.
• Implementation example:
• Buy 10,000 shares in 5 hours
• Place order for 500 shares every 15 minutes
• Improvements:
• Random lot sizes and intervals
• Offer more/less orders early on
• Adjust size based on market price
Volume Weighted Average Price
• VWAP is the volume-weighted average.
• Benchmark on trading price that gives large volume
transactions more weight in deciding the price.
• Implementation example:
• Buy 10,000 shares
• Place order for proportional to volume traded in
a 15-minute period every 15-minutes
Minimizing Impact Even More
• Routing orders to dark pools
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Private exchanges for trading securities
Not available to general public
No transparency
Came about to facilitate block trades when we want to minimize market
impact
Other Algorithms
• Cost-Driven Algorithms
• Minimize transaction and spread costs
• Also try to “time the market” right
• Opportunistic Algorithms
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•
•
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Take advantage of favorable market conditions
Liquidity driven
Pair driven
More on this later…
It’s a Business
• Firms like KCG (formerly GETCO) and Citadel offer execution services
• Deliver “price improvement” and “execution speed”
Understanding Execution Can Help Your Algorithm
• Understanding Execution Can Help Your Algorithm
• Improve your fill price
• Find others trying to “iceberg” and capitalize on this
Finding Hidden Liquidity
• Liquidity can be hidden in dark pools or in icebergs.
• Guerrilla algorithms try to find icebergs using probabilistic models
• Compare size and price of trades vs. order book quotes
• Identify patterns in order placement to identify a “source”
Readings
• Barry Johnson – Algorithmic Trading & DMA
Objectives
• Smart Execution
• Automating a Strategy
Objectives
• Smart Execution
Designing, implementing,
testing, and running an
• Smart automated
Execution trading strategy.
• Smart Execution
• Automating a Strategy
••Automating
Automatingaa Strategy
Strategy
In the Industry
Front Running
• Using small lots to find large, possibly iceberged orders.
• Send “ping” orders on one exchange to detect a hidden order and front
run by changing your order on other exchanges.
• Latency is important
Automated Market Making
• Place a buy limit order and a sell limit order above and below the spread
• Capitalize on the spread
• When automated, a few pennies of profit per transaction can reach
billions.
• Some exchanges offer rebates for market makers
• Fractions of a penny
• Improves market liquidity and narrows bid-ask spreads
Statistical Arbitrage
• Statistical Arbitrage capitalizes on opportunities that are not arbitrage in
the literal sense, but over a long period of time they will statistically be
near-riskless profit.
• Example – Index Arbitrage:
• Compare the price of an ETF to the weighted sum of its components
• Capitalize on price discrepancies, can predict the movement of the ETF
if there is a price mismatch
• Latency sensitive!
You can design, test, and run an automated
strategy!
Finding A Strategy
• The general ideas are simple and public
• The inner-workings, the securities to pick, the parameters, and the
technology are not
• Strategies can be found in academic papers, online forums, and blogs
Popular Brokers for Automated Trading
• Interactive Brokers
• TradeStation
• NinjaTrader
Free Trading Platforms
• NinjaTrader
• TradeStation (requires brokerage account)
• Quantopian
• Build Your Own!
Getting Market Data
• Free Minute Level Data
• Yahoo Finance
• Google Finance
• Free BATS Tick and Quote Data
• Netfonds – last 20 days on US Equities
• https://github.com/FEC-UIUC/Netfonds-Tick-Capture
• FEC – Captured last 3 months and counting
• Want free data? This link could be helpful.
• Low-cost Live and Historical Data Feeds
• Kinetick
• TickData
• More….
Testing A Strategy
• Back Testing – testing a strategy on historical market data
• Many trading platforms have built in backtesters
• NinjaTrader has a good one
• Quantopian has a good backtester for beginners
Testing A Strategy
• Performance Indicators
• Sharpe Ratio – measures strategy performance adjusted for risk
•
𝐸 𝑅𝑎 −𝑅𝑏
𝑉𝑎𝑟[𝑅𝑎 −𝑅𝑏
• The average rate of return versus a benchmark divided by the standard
deviation of returns.
• Why? Risk minimization is important too!
• A strategy that goes all in on 1:1 odds and happens to win is not a good
strategy.
• Max Drawdown – The maximum peak to trough distance on your P&L
• Given as a percentage
• Used to measure the risk in worst case
• Others: Alpha, Beta, Sortino Ratio, etc.
Testing A Strategy
• Pitfalls:
• Overfitting – Performs well on a specific timeframe or security, but bad on the
general case.
• Strategies can also fail to account for how they will impact the market
• Markets change. Strategy may have worked 4 years ago, but not now
• Solutions:
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Forward Testing – testing a strategy on live market data
Incorporate slippage – let some orders be filled at an unfavorable price
Having a large and recent data set
Train your parameters on a partition of your test data, verify accuracy on the
remaining portion
Momentum Following
• Idea: A security on an uptrend/downtrend will continue on an
uptrend/downtrend.
• Simple implementation:
• Take the derivative and second derivative of a moving average.
• When a threshold first/second derivative is crossed:
• Buy/sell security in an amount proportional to these two
parameters.
Mean Reversion
• Idea: Two or more securities that are co-integrated will revert to their mean
ratio when a divergence occurs.
• Simple implementation:
• Identify two co-integrated securities (i.e XLE and PFE)
• Run a linear regression on XLE vs PFE on the last X days
• If current spread is 2 standard deviations above the regression:
• Buy XLE, sell PFE
• If current spread is 2 standard deviations below regression:
• Sell XLE, Buy PFE
• If current spread is within 0.5 standard deviations of the regression:
• Liquidate your position
Readings
• Quantitative Trading: How to Build Your Own Algorithmic Trading Business –
Ernie Chan
• Algorithmic Trading & DMA – Barry Johnson
• Inside the Black Box – Rishi Narang
• Pairs Trading: Quantitative Methods and Analysis – Ganapathy Vidyamurthy
Quantopian
• https://www.quantopian.com/posts/mean-reversion-algorithm-for-clubuse
• A mean reversion strategy between XLE and PFE:
• Regresses the last X days, computes current spread’s Z-Score, and
compares the Z-Score to a threshold to make trading decision.
• TODO – For the next 15 minutes, improve the sharpe ratio, max
drawdown, and/or percent returns by tweaking the parameters at the
top or manipulating the trading logic!