Over the summer, I worked with my friends (and future co-founders) on developing a trading algorithm which implemented a complex machine learning technique. I was talking with my dad about the project and he mentioned some ideas of his own about small patterns he had recognized occurring for certain stocks. My dad is an experienced daytrader and strongly believes in his ability to beat the market. During that week, he found a pattern within the Tesla stock. If the current price was followed by 2 5-minute decreases in price but the volume of transactions increased during that time frame, the next 5-minutes was most likely going to see an increase in price. I was interested in the patterns he had seen and he asked me to build and test his algorithm since he could not code.
When I built his algorithm, which was very basic conceptually, I was surprised to find how tedious a straightforward concept could be to implement using python, a notoriously simple programming language. This is not to mention that I had already built a system to download stock data into a usable format. If my dad, or anyone without much coding experience, wanted to quickly test their stock trading ideas, they would have to first invest weeks learning how to code and building their algorithm. When looking for websites where he could test his idea without building a system from scratch, I found no products that my dad could easily understand. Pythia is an iOS app designed for investors like my dad, having some knowledge of the market and little to no coding experience. It allows traders to set dynamic conditions at which they wish to buy or sell a stock. Algorithmic trading is not a new concept. The most common introductory algorithmic trading strategy is to buy or sell when the price of a stock crosses its 52 week moving average. The moving average updates over time, leading the buy/sell price to change over time. Our product allows users to create complex trading algorithms with custom economic indicators while also keeping the interface intuitive for any trader. The Pythia app achieves this goal by exclusively using commonplace terminology (price, high, low, open, close, moving-average) arranged in a format closer to English than a programming language.
Additionally, anyone interested in algorithmic trading wants a way to test their algorithms before allowing it to manage their money. While this could be done through paper trading, trading with fake money on real time data, doing so would take months to get significant results. It is much more efficient to backtest the algorithms on readily available historical data. This allows the user to see the performance of their algorithm as if they had allowed the algorithm to manage money over some past time frame. Backtesting is the optimal method of verifying one’s algorithm works as it takes seconds. Furthermore, the hypothetical return of each algorithm can be easily compared to other algorithms and benchmarks such as the S&P 500 over the same time period. While discussing Pythia’s frontend with my dad, I found myself explaining the patterns an algorithm could account for by drawing the pattern in the air. If I, an investor who can code, would prefer to draw a pattern rather than explain it in technical terms and if my dad, an investor with no coding experience, could understand a drawn pattern more than a technical explanation, why don’t algorithmic trading apps allow users to draw patterns? Not only is drawing a stock’s pattern more understandable and usable, it is also fun. Instead of having to specify multiple price comparisons for multiple time points, a user can draw a line on their phone and immediately test it.
Pythia is an iOS app which aims to introduce young investors to algorithmic trading and provide a user focused interface for anyone interested in algorithmic trading, without requiring coding experience. This is done through holding the product to intuitive technical terms, implementing easily readable buy/sell conditions, allowing quick algorithm backtesting to measure performance, and simplifying pattern recognition to hand-drawn diagrams rather than code.
Pythia’s mission is to enable investors to start algorithmic trading. We believe that everyone should have the ability to apply quantitative analysis to their investing strategy without having to learn to code or build their own database. We’ve designed Pythia to make algorithmic trading friendly, approachable, and understandable for investors of all backgrounds.
As more young people begin to invest, managing one’s own portfolio is becoming more popular. On top of this, more young investors have adopted an active investing strategy, attributed to the introduction of commission free trading. Trading apps such as Robinhood show how individual, active trading is stepping into the spotlight, with people trading from their mobile devices instead of through a broker or more established company.
However, the rise of personal investing does not imply that traders are willing to forgo a sense of security and assurance while managing their accounts. Algorithmic trading can give people the confirmation they need when choosing what and how to invest. Using quantitative analysis, trading will not be dominated by an individual’s emotions. Algorithm backtesting, testing over historical data, allows investors to effectively measure the correctness of their trading schemas and efficiently compare one trading strategy with another.
Large funds have already been using computers to optimize and automate their trading strategies. We believe everyday traders need a tool to catch up. A tool which requires no coding experience. A tool which takes less than five minutes to understand. A tool which is simple, yet robust enough to capture the intricacies of the stock market. Pythia was carefully designed to meet the requirements of these everyday traders.
Young traders, algorithm traders with little to no coding experience, and everyday people with ideas they’d like to quickly try would benefit from the Pythia app since it’s algorithm creation system is designed to maximize intuitiveness rather than overload users with too many features.
Young traders would benefit by being able to automate many of their trading decisions. By automating their ideas to trade automatically, active traders will save time and effort as they will not need to spend time actively managing their portfolios. Young traders will also have the confidence to let their algorithms act on their behalfs because of Pythia’s backtest feature.
The backtesting feature coupled with the intuitive nature of Pythia’s user interface will benefit people looking to quickly test a trend they have noticed in the stock market. These users will not have to sort through an exhaustive list of features, many of which an everyday investor will not understand, provided by more established algorithmic trading applications. Instead, they will be able to easily pick up Pythia and use its everyday terminology to quickly build and then backtest their idea.
All users will benefit from Pythia as they will not be required to learn how to code or build a historical stock price database for backtesting. Whereas current algorithmic traders may have been forced to learn a programming language to begin building and testing algorithms, our iOS app does not require an initial investment of the trader’s time.
The potential market primarily derives from the newest round of individuals entering the stock market, Generation Z. BlackRock says “around 60% of gen X and millennials said they would consider [buying investments online], compared with less than half of Baby Boomers”. With openness to online trading increasing with newer generations, Gen Z, those born after the mid-1990s, is set to push trading companies towards website and app investing more than ever before. This trend is most evident through the mobile brokerage app RobinHood, which had approximately 6 million users at the end of 2019.
According to Andrew Kirillov, CFO and co-founder of TradingView, Generation Z is “the best prepared for the digitization of money management and financial services given their ability to navigate new technologies… Generation Z grew up with the artificial intelligence and automation tools that power modern investing solutions but may make older generations wary”. Gen Z’s comfort with AI, automation, and investing technologies such as robo-advisors indicate an emergence of a new potential market of automated trading. However, automated advisors are not enough to satisfy the demand of Generation Z traders. “A hands-on experience and real-time feedback are key for beginning teenage investors”.