Small Cap Quant Trader Grows $10K into $3.8M in 3 Years · Sam Miesse
Account Performance and Drawdowns
- Grew an experimental account from $10,000 to $3.8 million in under three years (2019–2021).
- Achieved approximately 800% growth in 2019 (from $10k to ~$86k) and grew to $2.2 million by the end of 2020.
- Suffered a specific daily drawdown of $500,000, attributed to position sizing anomalies rather than a model breakdown.
- Account performance was "flat" or a "wash" in 2022 and 2023 due to market regime changes, liquidity drying up, and the trader pivoting focus to game development and AI projects.
- Recorded ~100% growth in the current year (at time of interview).
Quantitative Strategy and Methodology
- Focused exclusively on small-cap stocks to maximize volatility and avoid direct competition with well-capitalized institutional investors in large caps.
- Developed a system processing over 10,000 small-cap stocks to generate 20 to 40 actionable daily opportunities.
- Utilized an intraday-only strategy, executing approximately 400 round-trip trades in 2019 and doubling the volume in 2020.
- The model is 90% data-driven and 10% subjective, primarily for final manual chart verification to filter out anomalies (e.g., buyouts or news-driven gaps) that automated systems miss.
- Execution requires entering within a specific one-minute window once a qualification trigger is met.
- Data collection took 1.5 to 2 years of manual record-keeping using ThinkorSwim on-demand features before statistical significance was confirmed.
- The system filters for "hyper-extensions" and parabolic moves, specifically shorting stocks that gap down or up significantly before mean reversion.
- Uses hard stops for risk management but manually executes exits to prevent market slippage.
Market Regime Analysis and Constraints
- Identified liquidity as a primary constraint; the model hit a "glass ceiling" where increasing position size began moving the market against the strategy.
- Attributes the 2020 super-charged returns to abnormal market conditions (stimulus checks, crypto, sports betting closure) that created artificial volume.
- Believes the 2020 gains were "robbing from the future," anticipating that the subsequent inflation and reduced retail capital (quiet recession) would suppress small-cap volume.
- Observes a divergence between large-cap indices (driven by institutions and "richer" participants) and small-caps (driven by retail traders suffering from inflation).
- Notes that sector correlation in small-caps creates "sympathy risk," where a large move in one stock can adversely affect multiple correlated positions simultaneously.
- Predicts that as economic sentiment improves and retail capital returns, the statistical edges in the model will regain potency.
Psychology, Risk Management, and Philosophy
- Emphasizes the necessity of "temperance," balancing the risk of being too conservative (paralysis by analysis) against being too aggressive (blowing up on black swan events).
- Describes quantitative trading as a method to remove subjective emotional bias (fear and greed) that plagued his early discretionary trading.
- Maintains that statistical edges exist despite "Efficient Market Theory" because human behavior remains repetitive and predictable, similar to predictive analytics in consumer tech sales.
- Warns against the "fraud" prevalent in the financial education sector, urging traders to verify claims via broker statements and look for transparency rather than flashy marketing (e.g., Lamborghinis).
- Advocates for "smelling out" fraud by identifying inconsistencies in narrative and behavior, citing specific legitimate traders who share P&L data openly.
- Believes that significant pain and failure (such as the $500k loss) are necessary for developing the resilience required to succeed in markets.
Future Outlook and Current Activities
- Currently transitioning from Puerto Rico back to the US, with real estate assets in Puerto Rico being converted to vacation rentals.
- Plan to re-engage deeply with quantitative trading in 2024, developing new models based on recent market observations.
- Continuing to pursue AI projects and game development alongside trading activities.
- Publicly sharing his journey via Twitter (handle: @apathetictrader) to provide an honest, data-driven counter-narrative to market fraud.