F I N A N C I A L E N G I N E E R I N G C L U B

A l g o r i t h m i c T r a d i n g

F i n a n c i a l E n g i n e e r i n g C l u b

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.

O b j e c t i v e s

• Smart Execution • Automating a Strategy

O b j e c t i v e s

• Smart Execution Deals with the execution of an order.

• Smart Execution • Used by large brokers, asset managers, etc. when placing orders • How can I place a large order and not get screwed?

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 • 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 • • • • 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

O b j e c t i v e s

• Smart Execution • Automating a Strategy

O b j e c t i v e s

• Smart Execution Designing, implementing, testing, and running an automated trading 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 𝑅 𝑎 −𝑅 𝑏 • • 𝑉𝑎𝑟[𝑅 𝑎 −𝑅 𝑏 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:

• 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-club use 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!