Elite Trader Insights: Garrett Drinon
Episode 17: Blending Discretionary Trading and Algo Strategies for Maximum Impact
Each week we try to unlock the collective wisdom of the Trading Elite. Strategies and unique insights from hours of interviews with top traders, sliced into bite-sized pieces and delivered to your inbox for your pleasure.
Garrett Drinon, once a successful musician, ventured into the fast-paced world of trading, carving a niche at a renowned proprietary trading firm. His journey from music to market speculation showcases his adaptability and creative application of skills across fields. Garrett's approach integrates both discretionary and algorithmic trading, aiming to leverage the best of both worlds to navigate the complex financial markets.
Key Learnings and Takeaways from the Interview
1. Systematic Approach to Swing Trades:
Garrett stresses the importance of having stringent exit rules in trading. After a personal experience of turning a large profit into a loss, he developed a rule-based strategy to cut positions systematically, either partially or wholly, based on specific market signals. This approach helps avoid emotional biases and ensures disciplined trading. Said that "When A, B, and C happens, I cut half, and when D happens, I cut the full thing, no matter what."
2. Development of Algos:
His transition to algorithmic trading stemmed from the need for precise and unemotional execution of these rules. Algorithms help him manage multiple trades simultaneously, especially useful during market openings when multiple signals may trigger at once.
3. Balancing Algos and Discretionary Trading:
While Garrett uses algorithms to streamline execution and manage multiple positions, he maintains the discretionary trading practice to keep a real-time pulse on market conditions and nuances that algorithms might miss. He also says it’s rare to find a great trader who understands quant, or a great quant to understands trading.
4. Importance of Market Environment:
Garrett highlights the adaptability of trading strategies to current market environments. His algo strategies are not static; they are continually adjusted based on ongoing market conditions and results, illustrating a dynamic approach to algo trading.
5. Pain Points in Algo Trading:
The biggest challenge in algo trading, according to Garrett, is the identification and exclusion of junk trades that do not fit the model but are picked up by the algorithm. This requires continuous refinement and understanding of the market to improve algorithm efficiency. He says "The algo catches the unicorn but also catches a million other terrible trades."
6. Growth and Sizing Challenges:
Garrett discusses the difficulties traders face in scaling their strategies effectively. He advises focusing on growing trade sizes during A+ setups to manage risk while capturing significant market opportunities.
7. Backtesting a Strategy
A strategy may not perform as well as what is shown on a backtest (particularly when trading stocks), and backtests often can’t take into account spreads, whether the order would have been completely filled etc. He also acknowledges that it’s incredibly hard to translate some discretionary trading strategies (around “Support / Resistance” for instance) into an algo.
8. Position Sizing is Crucial
“All pain in life is an indicator. It is a catalyst for change.” Garrett is a big fan of ‘Growth’ in your position sizing, while ensuring your size is not too big that you’re going to make emotional, discretionary errors and stuff the trade up, but also not too small that you’re going to be careless with the trade.
9. Trading is like Playing Music
When trading, you want to be on ‘autopilot’ similar to when you’re in flow state playing music. Your past experience and rules should be able to keep you in this zone, and need to ensure you’re not required to ‘think’ (ie What position size should I use this time? Where should I place my stop?), as these decisions pull you out of the flow state.
10. Networking and Collaboration:
Leveraging relationships and collaborating with smarter or more experienced colleagues such as Lance Breitstein and Mike Bellafiore has been crucial in Garrett's development as a trader. This collaborative approach has helped him refine his strategies and improve his trading outcomes.
This interview is a cracker. His perspective on entry around ‘Fight for the best price on a lower timeframe, but pay up on a higher timeframe’, I hadn’t heard before and thought he absolutely nailed that aspect.
Also loved the idea of waiting for the ‘Unicorn’ trades (for me personally, this is COVID, GFC, War, etc etc) but trading ‘Pony’ trades in the meantime, based on a smaller edge.
Garrett’s strategy really aligned with my thoughts particularly in terms of initially identifying a potential systematic trade based on your ‘Gut / Trading Experience’, then subsequently Data Mining / backtesting to prove / disprove, or find how the strategy may be improved.
Cheers
Marto
Nice one mate, interesting stuff.