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TABLE OF CONTENTS
TABLE OF CONTENTS
I. OVERVIEW
01. Introduction to Algorithmic Trading
02. Advantages of Algorithmic Trading
03. Algorithmic Trading Risks
04. Core Components to Build an Algorithmic Trading System
05. Semi-Automated Trading
06. 09 Steps to Develop Algorithms
II. PROPOSE AN ALGORITHM HYPOTHESIS
07. Distinction between Two Types of Algorithms
08. Execution Algorithms to Optimize Trading Fees
09. 06 Components to Build a Complete Trading Algorithm
10. Differences Between Equity and Derivative Trading
11. How to Construct an Algorithm Hypothesis
12. Trading Strategy Overview
13. Market-Neutral Strategy
14. Price Momentum Strategy
15. Mean-Reversion Strategy
16. Event-Driven Strategy
17. Market-Making Strategy
18. Scalping Strategy
19. ETF Front-Runner Strategy
20. Arbitrage Strategy
21. Grid Strategy
22. Smart-Beta Strategy
23. Sniffing Strategy
24. High-Tech Algorithmic Trading
25. Behavioral Finance in Algorithmic Hypothesis Formation
III. DATA
26. Standard Data in Algorithmic Trading
27. Data Cleaning Tutorial
28. Data Management in Algorithmic Trading
29. Stock Trading API in Vietnam Market
30. Search Process for Fastest Data Source
IV. BACKTESTING
31. Philosophical Foundations of Backtesting
32. Critical Mistakes in Backtesting
33. Backtesting Module
V. OPTIMIZATION
34. Optimizing Trading Algorithms
35. Techniques to Avoid Overfitting
36. Post-optimization Assessment
VI. FORWARD TESTING
37. Meaning of Forward Testing
38. Paper Trading
39. Small Account Test
VII. REAL TRADING
40. Algorithm in Real Environment
41. Evaluation of Execution Algorithms With Twap and Vwap
42. Measuring Implementation Shortfall
VIII. EVALUATION CRITERIA FOR ALGORITHMS
43. Return Rate
44. Maximum Drawdown in Algorithmic Trading
45. Kelly Criterion – Definition and Application
IX. OPTIMIZATION IN MULTI-ALGORITHM TRADING
46. Capital Optimization on Multi-Algorithms System
47. Beta Optimization
48. Leverage Transaction Data
X. ALGORITHMIC TRADING PRACTICE
49. Models or Randomness
50. Is Algorithmic Trading a Zero-Sum ame
51. What to Do When in Doubt of Trading Algorithm
52. Scams in Algorithmic Trading: 07 Major Characteristics
53. Third-Party Software in Algorithmic Trading
54. Is Algorithmic Trading Preferable for All Traders
55. How to Become an Algorithmic Trader
56. How to Learn Programming Skill for Algorithmic Trader
XI. INTEL CENTER – SUPPORT CHANNEL
57. Intel Center Overview
58. Foreign Trading Data
59. Daily Accumulative Foreign Trading Value in VN30
XII. ALGOTRADE LAB – FIRST STEP TO ALGORITHMIC TRADING
60. Algotrade Lab Overview
61. Introduction to SMA Algorithm
62. How to Register for API at SSI Securities JSC
63. Experience Algotrade Lab
64. SMA Algorithm Configuration and Monitoring Experience
OTHER ARTICLES
Sortino Ratio
Information Ratio
Fundamental Analysis and Technical Analysis
Smart Beta Backtesting Procedure
Biases in Algorithmic Trading
Popular Technical Analysis Indicators
Stock Price Prediction – A Discipline Branch Within Social Science
Challenges in Economic Forecasting
Five Personal Factors Influencing Investment Strategy
Feature Improvement Procedure for Live Algorithms
Overfitting and How to Mitigate It
Moving Average
How to Integrate Expert Input
Intraday Algorithmic Trading
Head and Shoulders Pattern
Algorithmic Trading, Quant Trading and High Frequency Trading
Equity Segmentation
Weighting Methods Used in Smart-Beta Strategy
Key Insights From Career Development of “Quant King” - Jim Simons
Passive, Active, and Quantitative Investment
Action Bias in Algorithmic Trading
Quantitative Hedge Funds and Algorithms
04 Strategies for Closing Positions in Algorithmic Trading
Hedging in Algorithmic Trading
Variations of Statistical Arbitrage
Optimal Position Sizing Strategy in Algorithmic Trading
What Algorithmic Trading Can and Can’t Offer for a Typical Hedge Fund
Implementation Challenges of Market Neutral Strategies
After The 9 Steps
Risk Management in Algorithmic Trading
Basic Assumptions and Market Evolution
GLOSSARY