Algorithmic trading is the automated execution of trading orders using computer programs and models. It aims to minimize costs, maximize fill rates, and reduce execution risk through faster and more reliable execution platforms and more accurate prediction models. Trends driving its growth include market electronification, a desire for anonymity and efficiency, and regulatory changes. Common algorithm types include arrival price, TWAP, VWAP, and MOC models. Areas of concern include lack of visibility, algorithms reacting to each other, and missing the trader's intuition. The process involves developing and testing trading strategies through backtesting before implementing them on execution platforms to trade.

2. • A pre-defined step-by-step method to accomplish a task
• A computer model that takes an order and structures a
sequence of trades
• Computer programs that generate buy and sell orders
and make lightning-quick trades
• It is the automated execution of trading orders decided
by quantitative market models.

3. Objectives:
• Minimize cost compared to a defined benchmark
• Maximizing fill rate
• Minimizing execution risk
• More reliable and faster execution platforms
(computer sciences)
• More comprehensive and accurate prediction models
(mathematics)

4. What are the trends behind it?
• Regulatory Changes
• Electronification of Markets
• Improve Scale & Efficiency
• Desire for Anonymity
• Realization that Trading Is a Source of “Incremental Alpha”
• Desire to Reduce Explicit and Implicit Trading Costs

5. Various Types of Algorithms in the Market
• Arrival price
• Time weighted average price (TWAP)
• Volume weighted average price (VWAP)
• Market-on-close (MOC)

6. Areas of Concern while setting Algorithms
• Lack of Visibility
• Algorithms Acting on Other Algorithms
• Which Algorithm to Use?
• Missing Ingredient—The Trader’s Gut Feel

7. What is the process?
1. Generate or improve a trading idea.
2. Quantify the idea and build a model for it.
3. Back test the strategy.
4. Collect the performance statistics.
5. If the statistics are not good enough, go back to #1.
6. If the strategy does not add significant value to the existing portfolio,
go back to #1.
7. Implement the strategy on the execution platform.
8. Trade.

8. Simple trading system design
a strategy a strategy a strategy a strategy a strategy a strategy a strategy
BROKER
Exchanges

9. What are the advantages?
• Move First
• Customise Quickly
• Rapidly Evolve
• Gain Access to Multiple Liquidity Pools
• Operate within Multiple Asset Classes
...

10. Cont…
• Integrate Real-time News into Algorithmic Trading
• Design for Low Latency Decisions
• Research and Back test Strategies
• Learn from Experience
• Integrate Risk Management with Algorithmic Trading

11. Issue with the Algorithmic Trade
• Filtration
• Consistency
• Internal Order Matching
• Rapid Strategy Implementation
• Safety

12. Conclusion
• Algo trading is a very competitive field in which technology is
a crucial factor.
• With the help of the algorithmic trading system the trade
activity becomes faster.
• But after all it is totally depends on the technology
• There are lots of example of crashing in the market due to
algorithmic trade system.
• So one has to not depend fully on the algorithmic system.

13. Thank
You…