https://github.com/soleinik/test-ml
Rust, ML, trading...
https://github.com/soleinik/test-ml
clap-rs linfa ndarray polars rust rust-lang
Last synced: 3 months ago
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Rust, ML, trading...
- Host: GitHub
- URL: https://github.com/soleinik/test-ml
- Owner: soleinik
- License: mit
- Created: 2024-01-09T19:04:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-25T23:18:27.000Z (over 1 year ago)
- Last Synced: 2025-01-25T07:27:34.275Z (5 months ago)
- Topics: clap-rs, linfa, ndarray, polars, rust, rust-lang
- Language: Rust
- Homepage:
- Size: 124 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# A Machine Learning test project
## About
The project's goal is to implement start to end file load with [polars](https://crates.io/crates/polars) and use [linfa](https://crates.io/crates/linfa) crates to learn and predict financial time series data, with [ndarray](https://crates.io/crates/ndarray) in between.
Project is build from two parts [CLI](/ml-cli/) and [library](/ml-lib/). For now [data](/data/) is in local folder that shoud be substituted, in future, with a financial data fetching API (i.e. [yahoo_finance_api](https://crates.io/crates/yahoo_finance_api))
## Status
Work-in-progress. At this moment only (list what is implemented)
Currently, only the following Linfa's algorithms implemented
- decision tree (`random forest`???), flags `-Ccdr`
- SVM (upport vector machines), flags `-Ccss`
works, that can be invoked as follows
```
$ ./target/debug/ml-cli -Ccdr -h
[r]andom forest learning algorithm
Usage: ml-cli AlgoCategory SL DT {RF|-r}
Options:
-h, --help Print help (see more with '--help')
```
actual run
```
./target/debug/ml-cli -Ccdr
accuracy:1
predicted:[1, 1, 1]
```
Progam will use time series data from [IBM, 5 years](./data/IBM-5y.csv) as base, [S&P500, 5 years](./data/^GSPC-5y.csv) as suplementary and attempt to learn and predict closing price trend for next 3 days.
NOTE: the goal is not actually make correct prediction, rather understand `polars`, `ndarray` and `linfa` interaction
## Compiling from source code (via git)
`test-ml` can be compiled directly from the git repository source code using the following method.
```
$ git clone https://github.com/soleinik/test-ml.git && cd test-ml
[...]
$ cargo build --release
```
If successful, this will produce a `ml-cli` executable located at `./target/release/ml-cli`
Executable (for now) expects `./data` folder to be available relative to execution root `.`
## Running:
### `Cargo`
```
$ cargo run -- -h
Demo CLI wrapper
Usage: ml-cli [OPTIONS]
Commands:
AlgoCategory, -C Algorithm's [C]ategory
help Print this message or the help of the given subcommand(s)
Options:
-v, --verbosity... log level
-h, --help Print help (see more with '--help')
-V, --version Print version
```
and follow help prompts... `help` is context sencitive. The above snippet demonstrates generic help prompt. The following snippet demonstrates help items for `Algorithm's category`
```
$ cargo run -- -C -h
Algorithm's [C]ategory
Usage: ml-cli {AlgoCategory|-C}
Commands:
SL, -c [s]upervised learning algorithms class
USL, -u [u]nSupervised learning algorithm class
partial-fit, -f Partial [f]it ML algorithms class
pre-processor, -p [p]re Processors ML algorithms class
help Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help
```
### Executable `ml-cli`
When successfuly built, executable will be located in either `./target/debug` or `./target/release` folder, depends on `--debug`(default) or `--release` flag passed to `cargo build`. Move executable `ml-cli` together with `./data/*` forder into same location. After that invoke it as follows
```
$ ./ml-cli -h
Demo CLI wrapper
Usage: ml-cli [OPTIONS]
Commands:
AlgoCategory, -C Algorithm's [C]ategory
help Print this message or the help of the given subcommand(s)
Options:
-v, --verbosity... log level
-h, --help Print help (see more with '--help')
-V, --version Print version
```
## Development pre-requisites
To _build & run_ this project you need to have the following installed on your system:
- Install **Rust** (latest stable) – [How to install Rust](https://www.rust-lang.org/en-US/install.html)
- Install **rustfmt**: `rustup component add rustfmt`
- Install **clippy**: `rustup component add clippy`
### Format checking (rustfmt)
Make sure your code is well-formatted by running:
```
cargo fmt
```
### Lint (clippy)
Lint your code (i.e. check it for common issues) with:
```
cargo clippy
```