This document discusses algorithmic trading and summarizes various techniques that can be used to develop and optimize automated trading systems including: - Using technical analysis signals like MACD, RSI, and %R to characterize market state along with sentiment analysis of news data. - Employing algorithms like genetic algorithms, neural networks, and random forests to improve trading strategies. - Predicting order book evolution by analyzing customized market data from Reuters including order book depth and price/volume information over time. - Applying machine learning techniques like regression trees, bagging, and genetic programming to build and optimize trading algorithms.

1. Algorithmic trading
Stefan Duprey

2. Develop and calibrate an automated trading system
using Reuters Market Data
• From Reuters data fetching to building
trading strategy
– Characterize market state using technical
analysis with a mix of signals (classical
financial indicator MACD-RSI-%R)
– Characterize market state using
Sentiment analysis from Reuters News
Sentiment
• High level algorithms to improve
trading strategies
– Use of genetic algorithms
– Use of neural network
– Use of bootstrapped aggregated trees

3. Using Reuters Data to predict order book
evolution
Pick-up and analyze customized relevant Reuters market data
• Reuters Market Data : from one depth real-time order book up to 5
• More comprehensive data available (Reuters News Analytics)
Bid 1,t Ask 1,t Bid size 1,t Ask size 1,t Bid 1,t-1 Ask 1,t-1 Bid size 1,t-1 Ask size 1,t-1
Order book depth 1 at t Order book depth 1 at t-1
Obs
Yield
at
t+dt

4. >> t = classregtree(X,Y);
>> Y_pred = t(X_new);
Regression Trees

5. Forests of Trees
predictors
up
down
down
up
up
up
down
up
down
up
up
.
.
.
response
Y
>> t = TreeBagger(nb_trees,X,Y);
>> [Y_pred,allpred] = predict(t,X_new);

6. Genetic programming in brief
Graphical wizard to build and calibrate optimization
• Online help to define and implement your algorithm
• Automatic code generation

7. Clustering, hidden markov chains and
non deterministic finite automaton

8. Sentiment Analysis
• Linguistics Lexalytics parser
• Help build an automate to quickly analyze news
• All assets targeted : IR, Bonds, Stocks,
Commodities, CPI, politics, …
• Trading frequency from milliseconds to yearly
rebalancing
Security ID : Identifies the market, such as an individual company, a stock index, or a currency market
Topic Code : Identifies the type of news, such as an earnings report, or a regulatory approval
Industry Code : Identifies the industry sector, such as the financial, manufacturing, or technology sectors
Geographic Code : Identifies the location of the news, such as North America, Europe, or Asia
News Type : Identifies the type of news release, such as an editorial, or a story
Additional tags : direction, relevance
Jan Feb Mar Apr May Jun Jul Aug
20
30
40
Price&sentiment
Feb Mar Apr May Jun Jul
-10
0
10
20
30
Jan Feb Mar Apr May Jun Jul Aug
0
2
4
6
P&L
-20 -10 0 10 20 30 40
0
0.05
0.1
0.15
0.2
0.25
Data
Density
sentiment_scoreday data
fit 1

9. GPU backtesting : daily averaged VWAP for 500 stocks over
2 years
• Benefit from the GPU high computation power
• Calibrate the backtesting according to your GPU
hardware
0 50 100 150 200 250
35
40
45
50
55
60
0 50 100 150 200 250
0
200
400
600
Weighted_Average_Bid_Price (𝑡) = 𝒌=𝟏
𝒅𝒆𝒑𝒕𝒉𝒔
𝒔𝒊𝒛𝒆 𝒌,𝒕 ∗𝒑𝒓𝒊𝒄𝒆(𝒌,𝒕)
𝒌=𝟏
𝒅𝒆𝒑𝒕𝒉𝒔
𝒔𝒊𝒛𝒆(𝒌,𝒕)

10. Trading decision engine

11. Results of Trading Strategy Test Object oriented programming for data filtering (time and tickers)
 Multiple data sources
 Back test and walk forward test (complete set of data)
 Trend following and cross-sectional momentum catch up strategy

12. Trading decision engine

13. Calling a FIX engine trading motor
from MATLAB using an API

14. Trading Toolbox
Market Access
Financial Toolbox
Optimization Toolbox
Trading Engine
Financial Toolbox
Statistics Toolbox
Risk Engine
Fin. Instruments Tbx
Financial Toolbox
Pricing Engine
Bloomberg EMSX
CQG
X_TRADER
Interactive Broker
Bloomberg EMSX