Video Summary
Systematic (quant) trading replaces emotional, discretionary decisions with predefined, data-backed entry and exit rules.
Evan found a directional edge in large percent gappers (about ~79–80% close below open) and built strategies around such patterns.
Robust sample sizes matter — Evan looks for at least ~300 samples and often uses thousands to avoid overfitting.
Backtesting and continuous data tracking (price, float, market cap, drawdowns) drive strategy selection and adaptation.
Position sizing uses a Kelly-like approach; algorithms reduce manual work but must be actively monitored for failures or market regime changes.
Systematic trading defines concrete, data-backed rules for entries, exits, stops and stock criteria. Evan switched after two years of discretionary trading losses because a rules-based approach removed emotion and produced consistent, testable edges.
He analyzed historical data and found patterns with strong probabilities — for example, large percent gappers on the day have roughly a 79–80% chance of closing below the open, giving a directional shorting edge.
Evan prefers a minimum of about 300 samples and often works with thousands to ensure statistical reliability and reduce overfitting risk.
He uses a Kelly-like approach: start with a defined bankroll, risk a percentage (e.g., ~10%), and increase risk after wins and decrease after losses to compound returns while managing drawdowns.
Generally no — Evan says news is 90–99% irrelevant for his strategies; quant algorithms prioritize measurable market data, though major, rare events can still matter.
Algorithms can have bugs, execution failures or unexpected drawdowns; Evan stresses continuous monitoring (e.g., a stop-loss cancellation incident) and tracking drawdown thresholds to flag when a pattern has broken.
"Price action is always trying to fool you; it's always trying to confuse you and take advantage of your emotions—your fear and greed."
Trading can often be an emotional journey where fear and greed can lead to mistakes. Price action can create illusions, making it hard to rely on gut feelings in trading decisions.
Systematic trading removes emotions from the equation, relying instead on data and numbers to dictate decisions.
"All my entries and exits are predefined based on data and the statistical edges."
Systematic traders, such as Evan Shunk, create a structured approach by defining their entries and exits prior to entering trades, offering a more reliable framework than discretionary trading, which often relies on intuition.
The process involves extensive data analysis conducted outside of market hours, allowing traders to refine their strategies based on statistical evidence.
"I was more of a discretionary trader... I traded for about two years and didn't really make any money until I switched to a systematic approach."
Many traders start with a discretionary trading style but may struggle to achieve consistent profitability. Evan experienced losses during his early years, totaling between $10,000 to $12,000 due to lackluster decision-making based on market feelings.
His shift to a systematic approach came after learning more about data tracking from successful traders, showcasing the importance of adapting trading styles to foster improvement and success.
"I found a statistical edge on paper; for example, large percent gappers on the day have about an 80% chance of closing below the open price."
Evan discovered that specific market patterns, such as the behavior of large percent gappers, provided predictable outcomes, allowing him to have a directional edge when initiating trades.
Understanding these statistical metrics gives traders insights into market tendencies, thus enhancing decision-making when determining whether to go long or short on a given stock.
"You're tracking just everything you can think of: all the price data, open, high, low, close, the market cap, the float—these things can change the winning percentage."
The speaker emphasizes the importance of tracking various types of historical market data, including price data (open, high, low, close), the time a stock reaches its daily high, market capitalization, and float.
They stress that understanding these variables is crucial for identifying factors that impact market trends and price movements. Additionally, they note some news pieces may be significant for tracking, although many are ultimately irrelevant.
"If I don't have a minimum of 300 samples, I'd feel pretty uncomfortable. Now I'm dealing with thousands of samples for each pattern."
The importance of using a robust sample size in data analysis is highlighted. The speaker asserts that having at least 300 samples is a personal threshold to ensure confidence and reliability in trading patterns.
By utilizing thousands of samples, the speaker has increased confidence in the integrity of their patterns and minimized the chance of overfitting based on insufficient data.
"You just track your pattern, everything about it... track all your trades, exactly what you did and what's not working, and always stick to your risk levels."
The process of becoming more systematic in trading involves meticulously tracking trade patterns and maintaining accurate records of all trades to analyze performance accurately.
The speaker suggests that traders start with specific metrics, such as gap-up percentages, and perform trials with various entry and exit points to discover successful strategies. Experimentation and testing are key to refining trading methods.
"It's good to have a team; share edges, things that you find in a small niche."
The speaker underscores the value of collaboration among traders to discover unique edges in the market.
Collaborating with others fosters a supportive environment where traders can share findings and insights, ultimately enhancing their trading strategies and decision-making processes.
"I remember his very first trade; it was my overnight short pattern at the time, and he made like $1,200."
The speaker recalls introducing a trading strategy to someone who had recently lost their job due to COVID-19. This individual made a profit on their first trade, igniting their enthusiasm for trading.
The initial success led this individual to follow the speaker's discretionary trades, but they quickly grew frustrated with the unpredictable nature of discretionary trading.
The speaker introduced a more systematic approach they had developed during the previous year, claiming it was highly effective and profitable on paper but had not yet been tested in practice.
"In 2020, we thought we cracked the code of the stock market."
The speaker and their trading partner experienced significant success using a basic strategy that capitalized on market gaps, leading them to feel like they had mastered trading.
However, the success was short-lived, as they encountered difficulties in 2022 when market patterns changed, resulting in substantial losses. This period served as a humbling reminder that market data can shift rapidly.
The speaker underscores the importance of continuously monitoring trading strategies and adapting to new market conditions.
"If I ever see a dip break its previous record of the largest drawdown, that’s a red flag."
The speaker explains their approach to evaluating trading patterns by tracking historical drawdowns and comparing them to past performance, identifying when a pattern may need to be abandoned.
They use data analysis tools, such as Excel charts, to monitor average gains and assess whether profits are staying above certain thresholds for sustainability.
The speaker acknowledges that occasionally, adjustments to a strategy are necessary, suggesting that sometimes layers within a pattern are ineffective and require modification.
"Algorithmic trading has given me so much more free time."
The speaker details their transition to algorithmic trading, which has drastically reduced the manual effort required in their trading process.
With a reliable algorithm managing trades, the speaker can focus on overall strategy and adapt to market conditions with minimal day-to-day involvement.
Even with an automated system, the speaker remains engaged by monitoring for errors and maintaining awareness of trade notifications, blending the efficiency of automation with active management.
"For my strategy, the news is 90-99% of the time irrelevant."
The speaker emphasizes that when utilizing a quant trading algorithm, news typically does not affect trading decisions. They stress that only a small fraction of news (around 1%) genuinely impacts the market, particularly in small-cap stocks, which are often flooded with irrelevant, recycled headlines.
In specific instances, like news surrounding significant companies such as Nvidia, they might reconsider trading decisions. However, the general approach is that detailed trading algorithms focus on quantifiable data rather than news events.
"I'd say it's more of a one-to-one risk-reward ratio."
The trader discusses their risk-reward ratio, indicating that their strategy tends to be more about achieving higher win rates rather than high risk-reward payouts for each trade. While they manage a statistical edge, they do not set high-risk targets, often facing scenarios where they might risk more than they tend to gain.
They explain their preference for a wide risk parameter, which can lead to situations where they may risk a higher percentage of their bankroll relative to the potential gains, sometimes engaging in trades with a risk of losing twice as much as the potential win.
"I use a Kelly system for mine."
The speaker outlines their method for determining position sizes, utilizing the Kelly criterion. By starting with a defined bankroll, they risk a certain percentage (e.g., 10%) on each trade. If their trades are successful, they incrementally increase the amount risked in subsequent trades, effectively compounding their profits.
Conversely, if they incur losses, their position sizes decrease. This dynamic adjustment allows them to manage risk effectively within their trading framework and optimize potential profit growth without overextending their resources.
"Our algorithm's still up; I turn it on every night before I go to sleep."
The trader utilizes a systematic approach to trading, running their algorithm continuously, which processes trades based on the pre-defined criteria regardless of market conditions. They mention that even during less favorable seasons for small-cap stocks, the algorithm continues to operate.
The speaker acknowledges the presence of market drawdowns, particularly during hot market conditions when they may experience squeezes. However, they choose not to turn off the algorithm during these periods due to the unpredictable nature of market movements and the inherent randomness in sector performance.
"Now that we're doing so well, I guess the competitiveness has gone down a bit."
The initial competitive dynamic between the speaker and their brother has shifted to a more collaborative approach.
Both parties are now working together on a common algorithm strategy, focusing on improving their trading systems collectively.
"The algorithm's not perfect; you've got to keep an eye on it."
Despite the automation of trading through algorithms, continuous monitoring is crucial due to the potential for errors and bugs.
The speaker emphasized the importance of vigilance to ensure that all trades are executed as intended and that their positions remain secure.
"One time, our stop-loss cancelled on us, and I didn't get filled out fully."
There have been significant trading events marked by both major wins and losses, with particular focus on a specific failure where a stop-loss order was canceled, leading to an unplanned exposure to risk.
The speaker noted the importance of real-time monitoring to mitigate such risks, as manual intervention was crucial in one instance to limit potential losses.
"We're always adapting with the market."
The speaker discussed the increasing fees associated with short selling and how their strategies must adapt to maintain profitability despite rising competition.
They pointed out that while short selling is becoming more crowded, they remain focused on their specific strategies and patterns which continue to yield results.
"Large gap percentages are always something we're looking at."
The speaker highlighted their current focus on trading strategies that take advantage of large gaps or significant price moves in the market, indicating a continued interest in short selling opportunities.
They acknowledged the importance of analyzing data for identifying potential short-selling edges, particularly in stocks exhibiting extreme price behavior.
"The best traders often have a discretionary aspect in their strategies, combining both systematic and discretionary approaches."
Traders discuss how certain strategies thrive based on market conditions, noting that stocks often exhibit volatile behavior, rising significantly before a downturn. The conversation highlights expectations surrounding potential rate cuts which could provide favorable conditions for trading, particularly on the short side.
The year 2020 is referenced as an anomaly where almost every trader found success with their strategies, paving the way for discussion on the attributes of successful traders.
"Most profitable discretionary traders are systematic in their approaches, yet adapt flexibly to the market."
An interviewee shares insights from a previous podcast, emphasizing that many profitable discretionary traders are actually systematic in their methods but incorporate a hybrid approach that allows for personal judgment. This blending of system and discretion is believed to elevate trading performance beyond what pure systematic approaches can achieve.
The conversation suggests that successful traders track significant data and statistical advantages but also rely on their intuition based on market sentiment and price actions.
"You can backtest your trading algorithms to see how they performed historically and how they might perform in the future."
The speakers explore the potential for creating algorithms that encapsulate their trading styles by defining entry and exit criteria. Emphasizing backtesting, they highlight its importance in validating strategies based on historical performance, especially when considering a shift to more swing trading.
They express a desire to capitalize more on swing trading opportunities, reflecting a shift from day trading strategies.
"With a trading bot performing trades, I spend far less time on manual analysis."
One trader describes their current daily routine, which has evolved thanks to algorithmic assistance. They mention waking up early to monitor pre-market conditions while allowing the bot to handle trades throughout the day.
Their process includes checking in on active trades primarily during the first hour and a half of the market, after which they have more free time for other activities.
"Tracking data and patterns is essential for building a disciplined trading strategy."
The trader offers advice for beginners, recommending they start by meticulously tracking data related to their trading patterns. This should include high, low, and close prices, along with various market indicators, to establish statistical edges.
For those starting anew with small accounts, the suggestion includes focusing on a single, proven trading pattern, emphasizing the need for disciplined practice while diversifying their entries over time to reduce volatility.
"I aim to make a million a year with my current trading systems."
A key aspiration shared is the goal of achieving significant annual profits through effective trading strategies and systems. The trader's brother serves as a benchmark, indicating that reaching such a financial milestone is indeed possible.
They affirm the necessity of diligence in achieving trading goals and highlight the ongoing technical challenges related to their trading bot that they are currently addressing.
"What do you think after hearing so much about Evan's story and his systematic trading approach? Are you tempted to adapt your current discretionary trading strategies to a more systematic and hands-off method?"
The conversation shifts to Evan's story and his systematic trading methodology, prompting viewers to reflect on their own trading strategies.
Listeners are encouraged to consider whether they might benefit from integrating systematic trading practices into their approach.
The podcast has previously featured interviews with various systematic traders, indicating a broader interest in this trading style.
For those interested in learning more about systematic trading, viewers are directed to an available playlist containing past interviews on the topic.
