Speaker: Abhijeet Biswas 

• Understanding Algorithmic Trading
• Myths and Benefits of Algorithmic Trading
• Algorithmic Trading Development Cycle
• Algorithmic Trading Infrastructure
• Skill Set for Algo Trading
• Careers in Algorithmic Trading

Speaker: Vagaram Chowdhary

• Preferred Coding Language – Speed vs easy to build.
• API’s – Data vendors and brokers.
• Data – Oil for an engine, Quality matters.
• Open Source tools – Optimisation tools against comprehensive testing.
• Algo system – features must have.
• What to focus on models or building system?

Speaker: Robert Carver

• What makes a good system
  – Technology
  – Backtesting and fitting
  – Execution
  – Operational and risk management
  – Sticking to the system
• Getting started with systematic trading

Speaker: Manish Jalan

• Most algorithmic trading strategies uses various combinations of technical studies and other factors to develop strategies in short, medium to long term horizon.
• Various live practical examples from the market and how it effect the overall profitability of the strategy will also be discussed.
• Audience will be able to relate their day to day trading with key statistics parameters and comprehend the do’s and dont’s of algorithmic trading.
• Presentation will also cover statistics in Options Market where price series have non-linear behaviour due to Options greeks involved. 
• Importance of statistics like standard deviations, correlations, mean and sharpe ratios which playsvital role in making a strategy long term profitable will be discussed.
• Large drawdowns and big flattish periods in algorithms can be avoided by using various mix of technicals with statistical factors.
• Alpha is often generated in Algorithmic trading when a mix of various statistics are applied both at the strategy level and portfolio level analytics.
• A technical algorithms can be made massively profitable if statistics is added to it in a systematic way.
• There are hidden statistics which if not taken into consideration cannot make an algorithm profitable.

Speaker: Sonam Srivastava

• Tactical Asset Allocation is an investment style where allocation to various asset classes is actively balanced based on market regimes. 
• The aim of the strategy is to to maximize portfolio returns while keeping market risk to a minimum, as compared to a benchmark index.
• The market regimes to be used are modeled using macroeconomic variables and short term technical signals. In a trending market regime risky assets like equities get higher allocation while in a risky market bonds and gold get higher allocation. 
• A tactical asset allocation strategy also tries to maximize the returns within the equity allocation choosing the best stock mix using equity market factors and modelling their behavior in various regimes.

Speaker: Vibhore Gupta

• Importance of Order Management System.
• How poor OMS can fail profitable strategy.
• How to design effective order management system.
• Impact of Order Management System over  strategy performance.
• Execution efficiency of Order Management System.
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Speaker: Mahendra Mehta

• Introduction.
• What is adaptive learning?
• Advantages for adaptive learning.
• How AI is deployed in adaptive learning.
• Examples from the Indian Market.
• Summary results.

Speaker: Dr Tom Starke

Speaker: Praveen Gupta

Speaker: Yuvraj Thakker

Speaker: Hrishav Sanghvi

• Crude Oil Scalping strategy – Impact of poor execution and slippage.
• Algo Risk management.
• Algo Regulations.
• Popular Algo platforms in India.
• Beginner and Institutional Architectures.
• How to setup an Algo desk?
• Predatory Bidding strategies.

Speakers: Shivaram K R and  Kusal Kansara

• How data is generated in huge quantities, mainly alternate data.
• Types of data and what kind of information can be found.
• The insights are stored in the data and have to be extracted.
• AI based models to create indicators.
• How to effectively do backtest to prevent overfitting.
• How to use ML on financial time series.

Speaker: Sandeep Tyagi

• What to automate and what to keep manual in an algo strategy?
• How to optimize using back testing?
• Managing risk of algo errors.

Speakers: Prateek Parashar and Phani Pavan Oruganti       

• How do we model for regime shifts; volatility regimes, price regimes and structural regimes.
• Adjusting to stock behavior; Each stock exhibits different characteristics that have to be considered in models.
• Usage of statistics to optimize trades, how to rely of statistics to aid technical study/factors.
• Risk reward and position sizing; most important aspect of trading. How to trail SLs and manage risk. 
• How to manage Delta of a trade. 
• How we overfit and also do not backtest in different market conditions to check for robustness.

Speaker: Kannan Singaravelu       

• A step by step guide to the data science workflow.
• Sequence modeling for time series.
• The problem of short-term memory.
• Building a trading strategy using neural networks.
• The hype and promise of AI