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• 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.
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)
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
Various Types of Algorithms in the Market


  • Arrival price

  • Time weighted average price (TWAP)

  • Volume weighted average price (VWAP)

  • Market-on-close (MOC)
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
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.
Simple trading system design

a strategy   a strategy   a strategy   a strategy   a strategy   a strategy   a strategy




                                       BROKER




                                       Exchanges
What are the advantages?
 • Move First

 • Customise Quickly

 • Rapidly Evolve

 • Gain Access to Multiple Liquidity Pools

 • Operate within Multiple Asset Classes
                                             ...
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
Issue with the Algorithmic Trade

  • Filtration

  • Consistency

  • Internal Order Matching

  • Rapid Strategy Implementation

  • Safety
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.
Thank
  You…

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Algorithmic Trading

  • 1.
  • 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.