1. Selecting your Tracking Portfolio.
2. Entering your Trades at the right time.
3. Exiting your Trades with a loss.
4. Exiting your Trades with a profit.
5. How much money to bet on each trade.
6. The Mindset to tie it all together.

1. What type of trading is better Discretionary or Nondiscretionary?
2. Can long term investing be done in a Nondiscretionary manner or Discretionary is the only way?
3. Fundamental Analysis can only be done in a Discretionary fashion?
4. Do Non-Discretionary Systems really remove emotion?
5. Do you think Discretionary Traders need a higher psychological edge and the results may be Multi-fold?

1. Capital Mind Momentum Portfolio Philosophy
2. Use of Quantitative Approach to run the Portfolio
3. Drawdowns compared to Indices / Benchmarks.

1. Trading strategy development workflow
2. Evaluating trading strategies - dos and don'ts
3. Meta strategies - balancing exploitation vs exploration

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1. The purpose of a Stop Loss
2. Different kinds of Stop Loss
3. Factors to consider when setting a Stop
4. Interaction between Stop Loss setting and Trading Speed
5. Factors determining correct Position Size
6. The Maths of Position Scaling

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1. We evaluate the use of Machine Learning models for optimisation of a portfolio of broad market indices and compare it with traditional models
2. We understand the advantages and disadvantage of using Machine Learning models
3. We look at various Machine Learning techniques - supervised learning, reinforcement learning & evaluate the efficacy
4. We see how we can evaluate the robustness of a ML model for portfolio construction

1. Under-Standing Greeks
2. Greeks Technicalities to help in the adjustment of trading style
3. Higher-Order Greeks
4. Normalizing Greeks for implementing time series analysis

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1. Applying what you've Learned - Why start a Business Today?
2. Hedge Funds vs. Prop. Shops
3. Fundraising - How to attract the right Investors into your Fund
4. Your Pitch - What do Investors look for? 
5. Hiring - full time vs. outsourced, Mistakes we made 
6. Strategy Selection and Development + Mistakes 
7. Data Selection + Mistakes 
8. Service Provider Selection + Mistakes 
9. What we did right
10. Some Hedge Fund horror stories for your entertainment

1. What is a Serverless setup for executing your code?
2. Why choose Lambda over EC2 for algo trading? 
3. Creating a webhook endpoint to receive chartink alerts via API Gateway
4. Calculating SL, Target and firing order using Zerodha APIs in AWS Lambda.
5. Setting up alerts to your telegram.

1. Is it possible to improve risk-adjusted returns of a portfolio by applying Quantitative Asset allocation tactics, I yes then How?
2. Does the same model and parameter work for every asset class or they have to be different for all?
3. Can we describe some simple rules?
4. Which will be the asset classes you would have in your model and why?
5. What options are available for Indian Investors and what should be the expected returns and risks of these models?

1. How to Develop Trading Strategy That Works?
2. How to Back Test?
3. How to Automate Trading Strategy?

1. Concept challenges
2. Tech challenges
3. Scaling challenges
4. Client expectations challenges

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1. Intraday gap vs day gap.
2. Impact of Intraday gap on price within the trading session.
3. Intraday Trading Strategies utilizing intraday gap measures

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1. Why risk management is the only holy grail in trading ?
2. Psychological Biases that hinder trading performance
3. Types of risks in trading, real examples and how to mitigate them ?
4. How to stress test to check robustness of a trading system ?
5. How to position size correctly to avoid risk of ruin ?

1. Choosing the right algo tool or automation platform
2. Automating your options trading on Quantiply.tech
3. Getting started in a systematic way
4. Simple risk management to keep the account from blowing up

1. Which strategies work? Markets that are not open to the public often have better trends.
2. Don’t get complicated. Limit yourself to these features: Trend and Volatility.
3. Daily vs intraday? Monthly? Bigger equity swings.
4. Learn to embrace risk. You cannot eliminate the risk.
5. How do you decide how much you should trade for every $10,000 investment?
6. How much diversification is necessary?
7. Not all stocks trend. You can tell by back-testing.
8. Selecting stocks for your portfolio: the ones that work best.

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1. How did you start your trading journey as a Turtle Trader?
2. You have worked with Market Wizards like Richard Dennis and William Eckhardt. How is the mindset of these people?
3. What were the Original Turtle trading rules, Do they still work?
4. What improvisations you did to the original rules.
5. What is the meaning of diversification in your eyes?
6. Trend Following is the game of riding outliers. How do we manage volatility in Trend Following Breakout Strategies?
7. How to select a timeframe for a Strategy?

1. About our API
2. Brief Roadmap
3. Trends in Retail Algo Trading
4. Scope for Fintech tie-ups

1. What are the factors Blume would look at during the investment process     - What are the factors an options trader would consider before putting on the trade - Greeks
2. How does Blume arrive at a valuation of the companies for which they get proposal     - Pricing of Options :- Intrinsic Value and Extrinsic Value
3. Can Blume put a stop loss on it's investments.  What is the typical winning ratio and how does it manage an investment that goes sour     - Using a Stop Loss for a Trade gone bad. Adjustments of Trades if market goes against

1. Introducing the problem
2. How AI helps?
3. Learnings that can be used for FinTech domain

1. How to make un-correlated trading strategies?
2. Using Martingale and Compounding together to keep losses small and winners big. 
3. Being both an option buyer and an option seller. Idea is to be profitable and not be pigeon-holed into a stereotype.

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1. Common Backtesting pitfalls
2. Real Execution results vs Backtested results
3. Why it's best not to trust great Backtests
4. ML in trading and why most academic ML for trading is useless
5. The best way for a retail trader or a small/medium size prop shop to use ML in trading

1. Understanding Volatility regime shifts  
2. Differentiate between trending and non-trending zones
3. Find volatility based support & resistances in the markets

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Learn about a simple yet powerful statistical technique based on randomness and how you can use it to:

1. Test the strength of your trading signals and avoid overfitting
2. Determine the optimal stock combination and weights for your portfolio
3. Know the reality of your backtest and prevent blow-ups by managing risk effectively
4. Price instruments with complex outcomes like barrier options

1. How to Execute your Trading System Signals to your Broker's Terminal, The Challenges, and Precautions
2. The When, Why and How of Scaling-In and Scaling-Out
3. How to handle Large Quantities orders while Entering and Exiting a Trade
4. The significance of Impact Cost Estimation. The Hero to Zero Myth

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