This was a side-project that I created in order to address crucial profitability issues in discretionary trading - when to take partials and at what return factor. The gif above shows the fully working example.
Partialling in trading (aka partial profit taking) is the act of partially closing a trade in order to capture profits, whilst still holding some shares in case the trade moves further in the trade's favour.
The issue with partialling is that, like all of discretionary trading, the trader's emotions play a huge role in the effectiveness of the strategy. Fear and greed are the two dominant emotions in the market, and they are the most influential factor when it comes to trade management.
In an attempt to mechanise the process and remove the emotional component from trading in the hope of increasing profitability, I created Day Trader Tools. This was a project that aimed to tackle the most important variable in any trading strategy, profitability, with a light hearted approach. To this end, I created a 'vending machine' that took advantage of animations (Framer Motion) in order to make the interactions visually appealing. ReactJS and ChakraUI were used for the user interface. Users would input their statistics, and my optimisation algorithm would provide them with the most favourable partialling strategy.
Users would provide their return factors matched to their probabilities (easily calculated from trade statistics) and purchase a token. The payment of the token was managed using Stripe, and so an AWS Lambda was necessary to utilise Stipe's service. On pulling the lever on the vending machine, the data would be sent to a series of AWS lambdas via API Gateway. The first Lambda was the request receiver, a NodeJS function that would immediately return a status 200 to the app. This lambda would create a new 'processing' status input in a DynamoDB table and then pass on the data to the 'PartialOptimiserLambda'. This is a lambda written in Python that would run the optimisation calculation. Python was chosen over NodeJS for the wealth of mathematical packages available. Whilst this python lambda was running in the cloud, the front end would poll the DynamoDB table via API Gateway and a NodeJS Lambda. Once the python lambda had finished its calculation, it would update the DynamoDB table with a status 'complete' and provide the optimisation results. The polling lambda would then return these to the front end, and they would be displayed in the 'results bucket'.
The platform was monetised such that each token would cost $4.99. The aim was, to some extent, to 'gamify' the interaction, and to take advantage of a throughput-based strategy over a subscription-based strategy.
This is a Next.js project bootstrapped with create-next-app.
First, run the development server:
npm run dev
# or
yarn devOpen http://localhost:3000 with your browser to see the result.
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API routes can be accessed on http://localhost:3000/api/hello. This endpoint can be edited in pages/api/hello.js.
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- Learn Next.js - an interactive Next.js tutorial.
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