During my time in London and subsequent move to Yorkshire, I dedicated several years to diligent work and pursued an education in the financial sector, fueled by the aspiration of one day establishing my own fund or achieving success in financial markets. Even though I wasn't certain about my exact purpose at that time, I consistently experimented with simple stock data visualisation techniques and discretionary trading strategies, gradually immersing myself in the realm of systematic and data-driven approaches. In 2018, I made a resolute decision to deepen my knowledge and transitioned entirely from manual (discretionary) trading to systematic, automated trading.
The pivotal moment came in 2019 when I participated in a quantitative trading championship as a novice "quant," seeking to measure my abilities against like-minded individuals. Surprisingly, I successfully qualified in the UK nationals, which provided a small glimmer of validation that perhaps I was on the right track. This encouraged me to continue striving and pushing myself further in the areas where it truly mattered, in order to carve a niche for myself.
It was during this period that I embraced the wisdom behind Nassim Taleb's philosophy on convex investing and trading. According to him, the path to wealth creation in the markets lies in "taking small losses and then achieving significant gains," owing to the occurrence of high-impact events known as black swans. This notion is closely related to the concept of a Convex Curve, where taking calculated risks and enduring some initial pain (a "slow bleed") can yield tremendous results in the face of uncertainty.
Convexity in alternative approaches to learning skills:
Rather than adhering strictly to a linear path, such as pursuing a conventional five-year education in a specific field, Taleb suggests exploring an alternative approach. He proposes adopting a "tinkering" method that involves engaging with a subject or skill and allocating small increments of time to experiment and assess one's natural inclination or talent. By doing so, individuals may discover a natural affinity and achieve remarkable progress within a relatively short span. Interestingly, I have personally experienced positive outcomes in this regard, despite having also spent 4 years pursuing a traditional University education. This alternative approach has allowed me to surpass my own expectations in specific areas of algorithmic trading within a few years' window, however most of the work was done within multiple months of consistent deep work.
Convexity in the competition:
Same example was competition, where there was practically no risk, other than the "pain" of learning a new programming language within a concise time frame, possibly due to the binary outcome of either qualifying or not. This is when the magic starts to happen. By adopting a tinkering approach and allocating small increments of time to experiment and assess my natural inclination or talent, I discovered a natural affinity for algorithmic trading and achieved remarkable progress within a relatively short span.
Convexity in Algorithmic Trading:
At Stag Strat, we have discovered a powerful concept that enhances our trading strategies: convex curves.
Enhancing Risk Management:
At Stag Strat, we understand the importance of effective risk management. By analysing the convexity of returns, we gain valuable insights into the risk-reward dynamics of our trading strategies. This enables us to identify strategies with favourable risk profiles. Our focus is on strategies that exhibit convex payoffs, where the potential for gains outweighs the risk of losses. This approach allows us to prioritise strategies with higher probabilities of delivering profitable outcomes while minimising downside risk.
Tailoring Position Sizing:
Proper position sizing is a key element in maintaining a balanced risk-reward profile. At Stag Strat, we leverage convex curves to adjust position sizes based on the convexity of our individual trading strategies. Strategies with convex payoffs justify larger position sizes, as the potential for outsized gains is higher. On the other hand, strategies with concave payoffs, where the potential for large losses outweighs the gains, may warrant smaller position sizes or even exclusion from our trading portfolio. By aligning position sizing with the convexity of our strategies, we optimize risk-adjusted returns.
Incorporating Dynamic Risk Controls:
Dynamic risk control is an essential aspect of our trading approach at Stag Strat. Convex curves play a crucial role in implementing these controls. For instance, we utilize trailing stop-loss orders that adjust based on the convexity of returns. As the market moves in our favour and the convexity of returns increases, we tighten the trailing stop-loss levels. This approach allows us to capture more significant potential gains while still protecting against adverse price reversals. By adapting our risk management
Portfolio Diversification:
Diversification is a cornerstone of our portfolio management strategy at Stag Strat. Convex curves help us achieve portfolio diversification by combining trading strategies with different convexity profiles. This approach allows us to build a diversified portfolio that benefits from convexity on a portfolio level. Strategies with positive convexity offset potential losses from strategies with negative convexity, resulting in a more stable and resilient portfolio. By reducing reliance on any single strategy, we navigate various market environments with a better risk-return balance.
Conclusion:
At Stag Strat, we leverage the concept of convex curves to optimise our algorithmic trading strategies in the Futures markets. By analysing the convexity of returns, we gain valuable insights into the risk-reward dynamics and identify strategies with favourable risk profiles. Our use of convex curves enhances risk management, enables tailored position sizing, incorporates dynamic risk controls, and embraces portfolio diversification based on convexity. This approach empowers us to achieve sustained success and maintain a strong position in the competitive landscape of algorithmic trading.