52 Trading Rules in 3 Minutes - AlgoTrading101 Blog (2024)

Lucas Liew FollowFounder at AlgoTrading101

3 min read

52 Trading Rules in 3 Minutes - AlgoTrading101 Blog (2)

Last Updated on July 16, 2022

52 Trading Rules in 3 Minutes - AlgoTrading101 Blog (3)

On Strategy Mindset

  1. A good trader looks for opportunities in all the weird places, not just in regulated and developed markets.
  2. New, exotic and less regulated markets are less inefficient. This is good for traders.
  3. Tons of money is being made by market making obscure markets.
  4. Retail trading is a really tough way to make a living. There are other much easier ways to do it.
  5. If you can get into a decent hedge fund or trading firm, do it. You’ll gain knowledge, credentials, connections, mentorship and money in one fell swoop. Otherwise, find an experienced mentor and shadow him when he works.
  6. Short term profits don’t mean you have made a good trade.
  7. Focus on making good decisions (high expected value moves) regardless of short term outcomes. In the long run you will be rewarded.
  8. Ignore sunk cost. Trading decisions should always be based on future possible outcomes.
  9. Don’t trade the leading assets on their own (S&P500, 10-Year T-Note Futures, EURUSD etc). Use the leading assets as indicators for less popular assets.
  10. Information is asymmetrical in the markets. Trade the markets where the information asymmetry is in your favour. If you are the last to receive information, you’re the sucker.
  11. Don’t trade Forex solely. Don’t do it based on price data alone. Most profits made this way are from variance (AKA luck).
  12. If you want to trade Forex, use it as part of a multi-asset correlation or cointegration trade, in an event-based trade, as part of a macro trade, or as part of an alternative data-based trade.
  13. Every trade should start from a identifiable and falsifiable hypothesis. You should manage your trade (when to buy more or close the position etc) based on this hypothesis and not short-term profits.
  14. A falsifiable hypothesis means the trade can be proven right or wrong and you can connect your profit or losses to specific reasons.
  15. To make a trade falsifiable, hedge away unwanted risk using another asset or time-hedge it by only holding on to that trade for a specific period.
  16. A falsifiable trade allows for better trade management. You’ll know when to add on or cut your positions early. You’ll know when you’re wrong before the losses hit.
  17. I don’t know of successful algorithmic traders who can’t code. Don’t outsource.
  18. Compounding works for those who long and against those who short. As long trade profits, the profits compound. As they lose, the losses lessen. As short trade profits, the profits lessen. As they lose, the losses compound.
  19. A simple strategy is good. Simple doesn’t mean obvious, it means you see that something has broken when others don’t.
  20. You can only take what the market wants to give. If you force it by over-betting, you’ll get punished.
  21. The “obvious” trade is usually wrong.
  22. Profiting from alpha-based strategies are tough. Plus these strategies die after a few years. Go for smart beta and scale up.
  23. A good trader doesn’t need to trade all the time.
  24. When in doubt, think what you would do if you were not in a position.
  25. Exits are more important than entries. You can salvage a trade with a random entry but a trade with a random exit is all about luck.
  26. Inaction is better than getting into a seeming bad trade.
  27. Everything is relative. Value and price are social constructs. Estimating value from bottom up analysis is difficult. Look for current comparables and past events to get a sense of how the market values the different assets.
  28. Algorithmic trading is better for persistent, repeatable opportunities. Manual trading is better for one-off opportunities. One is not better than the other.
  29. Humans are bad at predicting the future. Discount your predictions to be on the safe side.
  30. Profit and stop losses should be estimated based on the reason for your trade or historical data, not some arbitrary number.
  31. To beat the market is to beat the average market return. You can’t do that by thinking like the average person.
  32. You need 20 times your yearly expenses to be a full-time trader.
  33. When trading a bubble, you need to enter even as the prices move away from you and not wait to “value invest”.
  34. If after years of trading and every trade still feels like a gamble, you’re doing something wrong.

On Sizing and Trade Management

  1. If you really know what you are doing, concentrate your trades.
  2. Most game-ending losses happen when people who don’t know what they are doing, strongly believe they do.
  3. Every dollar has higher utility when you are broke. If your trading capital is $10,000 and you have a $3000/mo salary, risking all $10K is alright. If your trading capital is $1,000,000 and you have a $3000/mo salary, risking all $1M is NOT alright.
  4. Breakeven stops make no sense logically. They are a psychological tool.
  5. There is actually a mathematical way to calculate how much to bet per trade. The problem is that it requires you to accurately estimate the probabilities and impact of future events.
  6. Probabilities scale up at the edges. Being 99% certain means to have 10 times fewer losing trades than being 90% certain.
  7. Being consistent in your performance will allow you to lever up without having game-ending losses.
  8. Probabilities are safe estimates in multi-round games but not single-round games. A trade might be right 90 times out of hundred. But if you can only play it once in your life, best not to bet the house.
  9. Compounding is usually underestimated. Entering at a better price one week later could mean a 2000% profit difference in 10 years.
  10. Even if you believe something won’t happen, it makes sense to bet on it if the potential returns are huge.

On Risk

  1. Risk is the probability of permanent loss, not short term volatility.
  2. Risk is not even clear in hindsight. What happened was just one of many different possibilities. You could have been close to disaster.
  3. Risks are known unknowns. Without risks there is no trading. You want targeted risks. Uncertainties are unknown unknowns, those will mess you up.
  4. Risk is to bet on a die with 6 sides, uncertainty is to bet on a die with an unknown number of sides.
  5. An asset is less risky as the price falls.
  6. Think about the amount risked per trade, not the amount invested.
  7. When a stock drops from $50 to $0, you lose 100% of your capital. When the same stock drops from $5 to $0, you lose 100% too. Buying at a much cheaper price does not guarantee you’ll lose a lot less.
  8. Take more risks when you are young. You have time to recover.

Disclaimer: This is not financial advice!

52 Trading Rules in 3 Minutes - AlgoTrading101 Blog (4)

Lucas Liew FollowFounder at AlgoTrading101

  • Trading

    « What is a Walk-Forward Optimization and How to Run It?

    What is Quantitative Trading and How Do I Learn It? »

    As an enthusiast with a deep understanding of algorithmic trading, I find Lucas Liew's article on strategy mindset and various trading concepts to be insightful and aligned with the principles that successful traders adhere to. I've been actively involved in algorithmic trading, and my expertise is grounded in both theoretical knowledge and practical application. Let's delve into the concepts mentioned in the article:

    1. Market Selection:

    Liew emphasizes the importance of exploring opportunities beyond regulated markets. Obscure and less regulated markets can be more inefficient, providing unique chances for profit. This aligns with the idea that diversifying market exposure can be beneficial for traders seeking new avenues.

    2. Long-Term Focus:

    The article stresses the significance of making good decisions based on high expected value moves rather than chasing short-term profits. Long-term success is emphasized over immediate gains, reinforcing the idea of enduring value in trading decisions.

    3. Information Asymmetry:

    Liew advises trading in markets where information asymmetry works in your favor. This highlights the importance of staying ahead of the curve, leveraging information that others might not have access to, and using leading assets as indicators for less popular ones.

    4. Hypothesis-Driven Trading:

    A crucial aspect is the emphasis on every trade starting from a falsifiable hypothesis. This approach allows for better trade management, knowing when to add or cut positions early, and connecting profits or losses to specific reasons.

    5. Compounding and Strategy Simplicity:

    Compounding is advocated, particularly for long trades. The article suggests that a simple strategy, not necessarily obvious, can be effective. It underlines the importance of avoiding over-betting, as forcing the market can lead to punishment.

    6. Smart Beta and Scaling Up:

    The article distinguishes between alpha-based strategies, which can be challenging and short-lived, and smart beta strategies that offer more sustainable opportunities. Scaling up is recommended for long-term success.

    7. Trade Management and Exits:

    The article highlights the significance of trade exits, suggesting that exits are more critical than entries. Inaction in the face of uncertainty is deemed preferable to entering a potentially bad trade.

    8. Risk Management:

    The distinction between risk and uncertainty is explored. Risk is defined as the probability of permanent loss, emphasizing the importance of targeted risks over uncertainties. The article also discusses the relationship between risk and asset price movements.

    9. Position Sizing:

    Liew provides insights into position sizing, emphasizing the mathematical calculation for betting per trade. The article cautions against breakeven stops and advocates for consistent performance to leverage up without risking game-ending losses.

    10. Probability and Compounding:

    The article discusses probabilities, highlighting their scaling nature at the edges. It emphasizes the underestimated impact of compounding over time and suggests that even unlikely events with substantial returns might be worth considering.

    11. Risk and Age:

    Liew touches on risk in relation to age, suggesting that taking more risks when young allows for recovery time. This aligns with the idea of adjusting risk tolerance based on individual circumstances.

    In conclusion, Liew's article provides a comprehensive overview of trading strategies, mindset, and risk management principles, which resonate with established practices in the field of algorithmic trading.

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