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See how you can build trading apps with Slicematrix-io
Watch short videos on how to use SliceMatrix-io For Trading Stock Market
what is slicematrix-io(beta)?
SliceMatrix-IO(beta) is a machine learning platform. What makes SliceMatrix-IO(beta) extremely powerful is that the platform is designed to get users up and running fast with FEW LINES OF PYTHON CODE!
How does it work?
Users access SliceMatrix-IO(beta) via Python SDK which is designed to get users up and running fast with a few lines of code.
There are 2 core innovative concepts that allows users to build powerful machine learning with minimal code:
1. The Analytical Pipeline(AP) can be thought of as assembly lines of code which can train machine learning models. AP’s are reusable, meaning you can use the same AP to train multiple machine learning models using different input data.2. The Model
The model are where the real magic happens. After the Pipeline trains a Model, the Model live’s on SliceMatrix-IO(beta)’s machine learning infrastructure in the cloud. That means you can access your model after you create it in a distributed fashion. You can also train models in parallel subject to your usage plan’s request / throttling limits.
Use Machine Intelligence to identify hedge ratios
Real World use case
As any good pairs trader will tell you, hedge ratios are liable to change over time. One way to combat this problem is to use a rolling window of regressions: i.e. constantly updating your hedge ratio with a trailing window of data. The problem is this method is guaranteed to fail over time as you're always looking backwards, averaging what has happened in the past as your best guess of the future hedge ratio.
But a superior method is to employ a dynamic linear model such as the Kalman Filter (KF). The KF is the optimal model for estimating the parameters of linear gaussian models such as pairs trading.
Slicematrix pricing & How it works
Real World Use Case of how to calulate monthly cost
- One (1) initial call per pair to train the model and
- One (1) call to get the initial hedge ratio (using the "getState" method)
- One (1) call to update the model with every day's new closing prices (using the "update" method).
- This method will retrain the model with new information in the most optimal fashion and generate a new hedge ratio for the pair.
Assuming 20 trading days per month:
5000 / 22 calls per stock = ~227 stocks per month
i.e. you could generate daily hedge ratios for 227 stock symbols for just $25 per month.