Beginner’s guide to (part-time) system trading · Kory Hoang

Chat with Traders Episode 152: Corey Kwang's Trading Journey

Introduction to TradeStation and the Podcast

• The episode is sponsored by TradeStation, an online broker known for its trader-focused services, including competitive rates and advanced analytical tools.

• Host Aaron Fifield introduces the episode featuring Corey Kwang, a relatively new trader who has made notable gains in his trading journey.

Corey's Background and Early Experiences

• Corey was not a full-time trader at the time of recording but has since transitioned to trading full-time after leaving his job.

• He identifies as a retail systematic trader utilizing simple algorithmic strategies across various ETFs and cryptocurrencies.

Educational Foundation and Initial Trading Experience

• Corey studied finance and marketing at the University of Washington, which laid the groundwork for his interest in trading.

• His internship at Merrill Lynch provided him with valuable insights into financial markets, enhancing his understanding of stock market dynamics.

Transitioning from Internship to Trading

• During his internship, he shadowed experienced advisors, which inspired him to pursue a career in trading.

• Corey's early trading experiences involved self-research and learning about market sectors, leading him to start trading while still in college.

Initial Trading Strategies and Challenges

• He began trading technology stocks with limited capital during college but faced challenges when some investments did not perform well.

• Initially relying on gut feelings rather than structured methodologies led Corey to view his early trades as gambling rather than informed decisions.

Reflection on Early Trading Methodology

• Corey acknowledges that he lacked a systematic approach initially; he focused on trending stocks without following any established strategy.

• This lack of methodology contributed to losses when trades went against him, highlighting the importance of having a structured plan in trading.

Understanding the Transition from Discretionary to Algorithmic Trading

Initial Trading Approach

• The speaker describes their initial trading system as non-rigid and not scientifically structured, contrasting it with a scientific method that involves hypothesis testing and evidence collection.

• Many new traders start with intuition or hearsay about stocks rather than tested strategies, leading to unverified trading decisions.

• The speaker reflects on their early expectations of trading, believing they could easily achieve 1% daily returns, which is a common naive belief among beginners.

Shift Towards Algorithmic Trading

• The realization of unrealistic expectations led the speaker to explore algorithmic trading for a more systematic approach.

• A pivotal moment occurred in 2015 when the speaker watched a TED talk by Jim Simons, highlighting his success through mathematical algorithms in finance.

• Inspired by Simons' scientific methodology in trading, the speaker aimed to replicate this process for better results.

Learning Resources and Tools

• After being inspired by Jim Simons, the speaker sought resources to learn algorithmic trading effectively.

• They mention Quanto Peon as an essential tool for learning algorithmic strategies; it's an open-source platform based on Python that allows users to code their own strategies.

• Quanto Peon also features forums where users share ideas and collaborate on algorithms, enhancing community learning.

Development of Skills

• Initially lacking coding skills, the speaker learned through trial and error—copying existing algorithms and modifying them over time.

• Over months of practice using Quanto Peon’s resources—including lectures on building Python-based algorithms—the speaker improved significantly in algorithmic trading.

Community Engagement

• The quality of discussions within Quanto Peon's forum is highlighted as beneficial for gaining insights into algorithm development and improvement.

• The collaborative environment fosters learning among users who may not have prior experience but are eager to develop their skills in algorithmic trading.

Getting Started with Python on Quanto Pians

Initial Experience with Python

• The speaker had no prior experience coding in Python before discovering Quanto Pians, having only used Excel for basic tasks during business and finance courses.

• Despite the lack of experience, the speaker dove into coding, spending weeks learning to implement basic features like backtesting and adjusting stock symbols.

• The initial learning curve was steep but enjoyable; the speaker found passion in coding algorithms and often stayed up late working on them.

Transitioning to TradeStation

• The speaker transitioned from Quanto Pians to TradeStation due to its simpler coding language (Easy Language), which felt more familiar compared to Quanto Pians' tech-heavy interface.

• TradeStation offered better charting capabilities that aligned more closely with the speaker's previous experiences, making it easier to adapt.

Overcoming Challenges in Learning

• When faced with challenges, the speaker sought help from others who understood both finance and coding, though this proved difficult as these skill sets rarely overlapped.

• Collaborating through forums like Quanto Pians helped the speaker find individuals who could assist in implementing strategies effectively.

Gaining Confidence Through Practice

• After starting to code strategies in 2016, it took about six or seven months for the speaker to feel comfortable enough to invest real money into their algorithms.

• Initially investing small amounts ($5,000 - $10,000), they conducted walk-forward testing which built confidence as they observed their algorithm performing well.

First Automated Trading Experience

• While unable to recall their first automated trade specifically, the speaker remembers a significant win involving an investment in XIV (an inverse volatility ETN), which solidified their interest in automated trading.

Trading Strategies and Confidence

Initial Strategy Development

• The speaker discusses their journey in developing trading strategies, emphasizing that the chosen strategy was not the first attempt. They backtested various ideas before deciding to go live.

Confidence in Algorithmic Trading

• The speaker gained confidence from creating a profitable algorithm based on a simple moving average crossover, which worked well with specific assets and timeframes.

Embracing Automation

• Unlike many who may fear relinquishing control to computers for trading, the speaker felt at ease due to their extensive research into market anomalies and quantitative approaches.

Framework for Success

• The framework developed by the speaker has proven effective over one-and-a-half years of walk-forward testing and live trading, contributing significantly to their confidence in ongoing trading activities.

Algorithm Types and Market Analysis

Overview of Current Algorithms

• The speaker currently operates two types of algorithms: one focused on price anomaly detection (the "binocular") and another for executing trades (the "rifles").

Price Anomaly Detection Algorithm

• This algorithm scans securities using a proprietary metric to identify price anomalies, classifying them based on mean reversion or momentum properties. It helps avoid random walk assets.

Random Walk Assets Insight

• Approximately 95% of available assets exhibit random walk characteristics, making them unprofitable for most traders. Only about 5% show trends worth pursuing.

Hunting Down Opportunities

• After identifying potential trades with the binocular algorithm, the speaker uses simpler algorithms (the rifles) to execute trades effectively.

Understanding Market Efficiency

Concept of Market Efficiency

• The speaker references modern portfolio theory indicating that markets are mostly efficient; asset prices reflect all available information almost instantly.

This structured approach provides clarity on key concepts discussed in the transcript while allowing easy navigation through timestamps for further exploration.

Understanding Market Inefficiencies and Trading Strategies

The Concept of Market Efficiency

• The speaker reflects on their internship at Merrill Lynch, initially subscribing to the idea of market efficiency but later questioning its validity.

• They conclude that efficient assets are random, lacking extractable alpha or trading edges, while some assets exhibit inefficiencies with repeatable patterns.

Framework for Anomaly Detection

• The speaker describes using a price anomaly detection algorithm to identify exploitable assets, disregarding those deemed random.

• This algorithm functions as a scanning tool rather than executing trades, utilizing a technical oscillator to measure momentum expectancy and mean reversion.

Market Mapping and Asset Assessment

• The algorithm produces a market map plotting momentum expectancy against mean reversion expectancy, identifying asset behavior in different quadrants.

• Most assets fall into the "random walk zone," while others show persistent trends categorized as momentum or mean reversion anomalies.

Trading Strategy Development

• The speaker focuses on trading only a select few ETFs that demonstrate high risk-reward ratios based on identified anomalies.

• They emphasize the importance of understanding specific anomalies like momentum and mean reversion in developing effective trading strategies.

Learning from Industry Experts

• A pivotal moment occurred during QuantCon 2016 when the speaker learned about price anomalies from Manish Galan's lecture.

• Galan illustrated how different trading techniques apply to various assets using RSI indicators, highlighting the distinction between mean reversion and momentum techniques.

Understanding Momentum and Mean Reversion in Trading

Characteristics of Momentum and Mean Reversion

• The discussion begins with the concept of momentum versus mean reversion trading strategies, highlighting that if the RSI indicates overbought conditions, selling would align with a mean reversion strategy. Conversely, it raises questions about potential long positions as well.

• The speaker emphasizes the importance of identifying which assets exhibit momentum anomalies versus those that show mean reversion properties. They reference their own research on daily intervals, noting changes in asset behavior over time.

• Historical context is provided where a basic momentum system was effective until around the 1980s and 1990s when it ceased to work, leading to a shift towards mean reversion strategies during market downturns.

• The speaker shares personal findings from scanning various assets for strong momentum anomalies, including inverse volatility products and junk bonds, suggesting these can yield profits through simple RSI-based trading systems.

Scanning and Monitoring Assets

• A question arises regarding whether the scanning of different symbols is ongoing or based on initial research. The speaker clarifies they are developing a system for constant monitoring but currently take quarterly snapshots.

• Each quarter involves running scans across multiple time intervals (30 minutes to quarterly), generating XY plots that indicate which assets exhibit either momentum or mean reversion characteristics.

Strategy Development Based on Asset Behavior

• Summarizing their approach: every quarter, an algorithm identifies stocks or ETFs with strong characteristics of either mean reversion or momentum. This informs their trading strategies based on asset behavior.

• The speaker argues that successful trading isn't solely about perfect indicators or money management systems; rather, it lies in understanding the underlying asset's behavior patterns over time.

• They stress that random assets are challenging to trade effectively; however, recognizing consistent patterns allows traders to capitalize on them repeatedly.

Community Engagement and Clarification

• The speaker expresses willingness to answer questions from listeners but notes limitations due to their day job while also engaging in algorithmic trading part-time.

• They acknowledge respect for their time constraints while encouraging clarification requests about their framework without revealing proprietary details.

Generating Trading Ideas

• When asked how they generate strategy ideas after classifying assets by characteristics, the speaker hints at drawing inspiration from personal life experiences as part of their creative process in developing trading strategies.

Background and Influences on Trading Strategies

Personal Background

• The speaker shares their background, having been born and raised in Vietnam before moving to the United States in 2004 at age 12. They grew up under a hybrid system of communism and capitalism.

Learning from History

• In Vietnam, the speaker learned extensively about the Vietnam War, particularly guerrilla tactics used by the Vietcong against American forces. This historical context influenced their approach to strategy development.

Guerrilla Tactics as a Metaphor for Trading

• The speaker draws parallels between guerrilla warfare strategies and trading, emphasizing that one should not confront superior forces directly but instead focus on vulnerable targets.

Simplifying Strategy Development

• They advocate for simplicity in trading strategies, suggesting that focusing on a few key areas where one excels is more effective than trying to master everything.

Effectiveness of Simple Strategies

• The speaker notes that despite their simplicity—often involving only two or three variables—their strategies yield robust results over time.

Technical Indicators and Their Application

Approach to Technical Analysis

• Most of the speaker's strategies revolve around technical indicators; however, they differentiate their method from traditional technical analysis which often relies heavily on subjective interpretations.

Quantitative Use of Indicators

• Instead of drawing trend lines or identifying patterns subjectively, they use cycle indicators quantitatively as triggers for trades based on specific thresholds (e.g., RSI levels).

Parameters in Strategy Creation

• When creating strategies, the speaker prefers fewer inputs—typically less than ten—to avoid overfitting or data mining issues. Inputs may include parameters like RSI length or moving average settings.

Exit Strategies and Trade Management

Exit Conditions Based on Indicators

• The exit conditions for trades are primarily determined by indicator signals rather than fixed stop-losses or profit targets. For example, selling when RSI indicates overbought conditions.

Mean Reversion Systems Explained

• The speaker explains that mean reversion systems require flexibility; using strict stop-losses can lead to premature exits from trades. Allowing some room for fluctuation is essential for capturing full moves.

Historical Performance Example

• An example is provided where using a two-period RSI with specific buy/sell thresholds yielded high Sharpe ratios over 10–15 years compared to simply holding an asset like SPY during market downturns.

Live Trading Strategies and Lessons Learned

Duration of Live Trading Experience

• The speaker has been live trading algorithms for approximately 18 months, starting in July 2016.

• They have occasionally overridden their strategies, particularly after the 2016 election due to doubts about market reactions.

Key Learning Moments

• A significant lesson was learned when the speaker turned off their algorithm post-election, missing out on substantial gains (30-40%).

• The speaker aims for a good annual return of around 60-70%, based on backtesting seven different algorithms.

Performance Expectations and Risks

• While aiming for high returns, the speaker acknowledges that trading inverse volatility securities can lead to both incredible gains and disastrous losses.

• The speaker emphasizes the importance of using algorithms rather than manual trading to mitigate risks associated with these securities.

Monitoring Strategies

• Due to a full-time job, monitoring is done remotely; however, there are challenges in ensuring consistent connectivity.

• An incident during a vacation highlighted the need for regular checks as an API disconnection led to a minor drawdown.

Balancing Work and Trading

• Despite not achieving target returns this year (17%), the risk-adjusted return is favorable with a Sharpe ratio of about 2.0.

• The speaker finds it manageable to balance algorithmic trading with their day job, checking performance periodically without significant stress.

Hedge Fund Aspirations and Charitable Initiatives

Plans for a Hedge Fund

• The speaker discusses their ambition to start a hedge fund, emphasizing the need to build a track record before launching.

• They are currently focused on developing strategies that will be implemented in the hedge fund.

Charity Contributions

• The speaker introduces their startup, Quant Profit, which collects various quantitative investment strategies from online sources.

• Strategies are vetted and tested before being made available for subscription on their platform, allowing users to receive trading signals.

Trading for Charity Program

• A charity initiative called "Trading for Charity" was launched with an initial seed of $30,000 linked to the trading algorithm's profits.

• Profits generated by the algorithm will be donated to "Step Up for Laos," a nonprofit organization providing prosthetic limbs to children affected by unexploded ordnance (UXO).

Personal Connection and Motivation

• The speaker shares personal ties to the region impacted by UXO and expresses empathy towards victims who suffer severe injuries from landmines.

• They highlight the importance of corporate social responsibility in business ventures and hope to inspire others in trading communities to contribute positively.

Contact Information and Community Engagement

• For those interested in reaching out, the speaker provides their email addresses: hoang.kory@gmail.com and info@quantprofit.com.

• They mention active participation in the Chat With Traders Facebook group as a way for traders to connect.