Introduction to Algorithmic Trading Strategies Lecture 1 Overview of Algorithmic Trading
Haksun Li firstname.lastname@example.org www.numericalmethod.com
Definitions IT requirements Back testing Scientific trading models
Haksun Li CEO, Numerical Method Inc. Quantitative Trader/Analyst, BNPP, UBS PhD, Computer Science, University of Michigan Ann Arbor M.S., Financial Mathematics, University of Chicago B.S., Mathematics, University of Chicago
Quantitative trading is the systematic execution of trading orders decided by quantitative market models. It is an arms race to build
more reliable and faster execution platforms (computer sciences) more comprehensive and accurate prediction models (mathematics)
Market Making Quote to the market. Ensure that the portfolios respect certain risk limits, e.g., delta, position. Money comes mainly from client flow, e.g., bid‐ask spread. Risk: market moves against your position holding.
Bet on the market direction, e.g., whether the price will go up or down. Look for repeatable patterns. Money comes from winning trades.
Risk: market moves against your position holding (guesses).
Build or buy a trading infrastructure.
many vendors for Gateways, APIs Reuters Tibco
Collect data, e.g., timestamps, order book history, numbers, events.
Reuters, EBS, TAQ, Option Metrics (implied vol), flat file, HDF5, Vhayu, KDB, One Tick (from GS)
Clean and store the data.
Gateways to the exchanges and ECNs.
ION, ECN specific API Aggregated prices
Communication network for broadcasting and receiving information about, e.g., order book, events and order status. API: the interfaces between various components, e.g., strategy and database, strategy and broker, strategy and exchange, etc.
STP Trading Architecture Example
xchanges, ., Reuters, oomberg existing syste
CFETS: FX, bonds
Back-office, e.g., settlements
Other Trading Systems
Algo Trading System
Unified Trade Feed Adapter, CSTP
Trading System Adapter
Booking System Adapter
Main Communication Bus
Market Data 9
RMB Yield Curves
Trade Data Database
Centralized Database Farm
1. 2. 3. 4. 5. 6. 7. 8.
Generate or improve a trading idea (intuition). Quantify the idea and build a model for it. Back test the strategy. Collect the performance statistics. If the statistics are not good enough, go back to #1. If the strategy does not add significant value to the existing portfolio, go back to #1. Implement the strategy on an execution platform. Trade.
Sample Trading System Design
Separation of responsibilities.
Simplify coding of the trading logic.
Mimic how a human trader and broker work.
• implements the trading logic; • needs not wait/block for handshake messages from the exchanges.
• handles all the complicated order routing protocols with the exchanges • acts an internal market to aggregate and reuse orders to optimize execution; • acts as a guard to catch errors.
Rapid Strategy Implementation Problem
We want to release a strategy to production in hours if not sooner after research. Our experience is that the majority of the code is about order manipulations.
This is especially true for high frequency trading for which clever order manipulations are necessary to reduce slippage.
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