Why Building a Trading Algorithm is More Than Just the Algorithm - 3 Things

1

Kaggle

It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like backtesting.py or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.

#Data Collaboration #Data Dashboard #Databases 99 social mentions

2

Yahoo! Finance

It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like backtesting.py or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.

#Finance #Trading #Investing 206 social mentions

3

Alpaca Trading API

Let’s take, for example, the crypto world. In this space, there are over ten difference crypto exchanges all with their different ploys and different opportunities, including Binance, Coinbase, FTX, Kraken, KuCoin, and much, much more. Some originate in Fiat, others originate in crypto. Moreover, what if you wanted to switch your trading algorithm to run on forex or stocks? The list of exchanges increase by the dozen, and each one is its own independent entity. These kinds of questions, though a little less important in a backtesting environment, become imperative in a live environment. In order to make trades, you HAVE to connect to an exchange or brokerage... there’s no way around it. For many, the connection to an exchange is completely skipped over during the testing and experimental stage. Again, de facto-ing to downloading datasets is great, but how will you operate on the logic of making trades? How are you going to switch your algorithm into a paper trade environment for every exchange? These are critical questions to ask yourself as you implement your trading algorithm: what asset are you going to use, what exchange are you going to integrate with, and how are you going to actively manage and maintain that exchange as it changes its API (look at Alpaca, they’ve been constantly changing their APIs and systems).

#Finance #Trading #Fintech 57 social mentions