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https://github.com/dylanrichardson/trading-system
Automatic trading system using machine learning
https://github.com/dylanrichardson/trading-system
Last synced: about 2 months ago
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Automatic trading system using machine learning
- Host: GitHub
- URL: https://github.com/dylanrichardson/trading-system
- Owner: dylanrichardson
- Archived: true
- Created: 2018-01-24T00:00:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-09T02:48:34.000Z (over 4 years ago)
- Last Synced: 2024-07-30T02:23:13.798Z (about 2 months ago)
- Language: Python
- Size: 73.2 KB
- Stars: 7
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# trading-system
Automatic trading system using machine learning
## Setup
### Clone
```
git clone [email protected]:drich14/trading-system.git
```
### Secrets
```
export alphavantage_key=
export intrinio_username=
export intrinio_password=
```
### PyEnv (optional)
Activate
```
pyenv activate venv
```
Deactivate
```
pyenv deactivate
```
### Install dependencies
```
pip install -r requirements.txt
```
### Save new dependencies
```
pip freeze > requirements.txt
```
### Run tests
```
python tests.py
```
## Programs
### graph.py
```
usage: graph.py [-h] [-s SYMBOLS [SYMBOLS ...]] [-y SCREENER]
[--percentages PERCENTAGES [PERCENTAGES ...]]
[--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]]
[--training_screener TRAINING_SCREENER]
[--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]]
[--validation_screener VALIDATION_SCREENER]
[--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]]
[--evaluation_screener EVALUATION_SCREENER] [-l LIMIT]
[--start START] [--end END] -o OPTIONS [OPTIONS ...]
[-t TOLERANCE] [-d DAYS] [-e EPOCHS] [-n NODES] [-a {tanh}]
[--loss {mean_squared_error}] [-p] [-v] [--path]
{data,optimal,neural}
Load a graph.
positional arguments:
{data,optimal,neural}
data to graph
optional arguments:
-h, --help show this help message and exit
-s SYMBOLS [SYMBOLS ...], --symbols SYMBOLS [SYMBOLS ...]
symbol(s)
-y SCREENER, --screener SCREENER
name of Yahoo screener
--percentages PERCENTAGES [PERCENTAGES ...]
relative size of each data part
--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]
symbol(s) to train with
--training_screener TRAINING_SCREENER
name of Yahoo screener to train with
--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]
symbol(s) to validate with
--validation_screener VALIDATION_SCREENER
name of Yahoo screener to validate with
--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]
symbol(s) to evaluate with
--evaluation_screener EVALUATION_SCREENER
name of Yahoo screener to evaluate with
-l LIMIT, --limit LIMIT
take the first l symbols
--start START start date of data
--end END end date of data
-o OPTIONS [OPTIONS ...], --options OPTIONS [OPTIONS ...]
indices of data_options in params.py to use
-t TOLERANCE, --tolerance TOLERANCE
tolerance to use in optimal trades algorithm
-d DAYS, --days DAYS number of prior days of data to use as input per day
-e EPOCHS, --epochs EPOCHS
number of epochs to train for
-n NODES, --nodes NODES
number of nodes per layer
-a {tanh}, --activation {tanh}
type of activation layer
--loss {mean_squared_error}
type of loss function
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
Example
```
python graph.py data -s AAPL -o sma --start 2018-01-01 --end 2018-02-01 -p
```
### neural.py
```
usage: neural.py [-h] [-s SYMBOLS [SYMBOLS ...]] [-y SCREENER]
[--percentages PERCENTAGES [PERCENTAGES ...]]
[--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]]
[--training_screener TRAINING_SCREENER]
[--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]]
[--validation_screener VALIDATION_SCREENER]
[--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]]
[--evaluation_screener EVALUATION_SCREENER] [-l LIMIT]
[--start START] [--end END] -o OPTIONS [OPTIONS ...]
[-t TOLERANCE] [-d DAYS] [-e EPOCHS] [-n NODES] [-a {tanh}]
[--loss {mean_squared_error}] [-p] [-v] [--path]
Create a neural network.
optional arguments:
-h, --help show this help message and exit
-s SYMBOLS [SYMBOLS ...], --symbols SYMBOLS [SYMBOLS ...]
symbol(s)
-y SCREENER, --screener SCREENER
name of Yahoo screener
--percentages PERCENTAGES [PERCENTAGES ...]
relative size of each data part
--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]
symbol(s) to train with
--training_screener TRAINING_SCREENER
name of Yahoo screener to train with
--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]
symbol(s) to validate with
--validation_screener VALIDATION_SCREENER
name of Yahoo screener to validate with
--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]
symbol(s) to evaluate with
--evaluation_screener EVALUATION_SCREENER
name of Yahoo screener to evaluate with
-l LIMIT, --limit LIMIT
take the first l symbols
--start START start date of data
--end END end date of data
-o OPTIONS [OPTIONS ...], --options OPTIONS [OPTIONS ...]
indices of data_options in params.py to use
-t TOLERANCE, --tolerance TOLERANCE
tolerance to use in optimal trades algorithm
-d DAYS, --days DAYS number of prior days of data to use as input per day
-e EPOCHS, --epochs EPOCHS
number of epochs to train for
-n NODES, --nodes NODES
number of nodes per layer
-a {tanh}, --activation {tanh}
type of activation layer
--loss {mean_squared_error}
type of loss function
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
Example
```
python neural.py -s AAPL -o sma --start 2018-01-01 --end 2018-02-01
```
### optimal.py
```
usage: optimal.py [-h] [-s SYMBOLS [SYMBOLS ...]] [-y SCREENER] [-l LIMIT]
[--start START] [--end END] [-t TOLERANCE] [-p] [-v]
[--path]
Load optimal trades.
optional arguments:
-h, --help show this help message and exit
-s SYMBOLS [SYMBOLS ...], --symbols SYMBOLS [SYMBOLS ...]
symbol(s)
-y SCREENER, --screener SCREENER
name of Yahoo screener
-l LIMIT, --limit LIMIT
take the first l symbols
--start START start date of data
--end END end date of data
-t TOLERANCE, --tolerance TOLERANCE
tolerance to use in algorithm
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
Example
```
python optimal.py -s AAPL
```
### preprocess.py
Usage
```
usage: preprocess.py [-h] [-s SYMBOLS [SYMBOLS ...]] [-y SCREENER]
[--percentages PERCENTAGES [PERCENTAGES ...]]
[--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]]
[--training_screener TRAINING_SCREENER]
[--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]]
[--validation_screener VALIDATION_SCREENER]
[--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]]
[--evaluation_screener EVALUATION_SCREENER] [-l LIMIT]
[--start START] [--end END] -o OPTIONS [OPTIONS ...]
[-t TOLERANCE] [-d DAYS] [-p] [-v] [--path]
Preprocess neural network data.
optional arguments:
-h, --help show this help message and exit
-s SYMBOLS [SYMBOLS ...], --symbols SYMBOLS [SYMBOLS ...]
symbol(s)
-y SCREENER, --screener SCREENER
name of Yahoo screener
--percentages PERCENTAGES [PERCENTAGES ...]
relative size of each data part
--training_symbols TRAINING_SYMBOLS [TRAINING_SYMBOLS ...]
symbol(s) to train with
--training_screener TRAINING_SCREENER
name of Yahoo screener to train with
--validation_symbols VALIDATION_SYMBOLS [VALIDATION_SYMBOLS ...]
symbol(s) to validate with
--validation_screener VALIDATION_SCREENER
name of Yahoo screener to validate with
--evaluation_symbols EVALUATION_SYMBOLS [EVALUATION_SYMBOLS ...]
symbol(s) to evaluate with
--evaluation_screener EVALUATION_SCREENER
name of Yahoo screener to evaluate with
-l LIMIT, --limit LIMIT
take the first l symbols
--start START start date of data
--end END end date of data
-o OPTIONS [OPTIONS ...], --options OPTIONS [OPTIONS ...]
indices of data_options in params.py to use
-t TOLERANCE, --tolerance TOLERANCE
tolerance to use in optimal trades algorithm
-d DAYS, --days DAYS number of prior days of data to use as input per day
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
Example
```
python preprocess.py -s AAPL -o sma --start 2018-01-01 --end 2018-02-01
```
### screener.py
Usage
```
usage: screener.py [-h] [-l LIMIT] [-p] [-v] [--path] screener
Screen for symbols.
positional arguments:
screener name of Yahoo screener
optional arguments:
-h, --help show this help message and exit
-l LIMIT, --limit LIMIT
take the first l symbols
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
Example
```
python screener.py -l 10
```
### symbol.py
Usage
```
usage: symbol.py [-h] [-s SYMBOLS [SYMBOLS ...]] [-y SCREENER] [-l LIMIT]
[--start START] [--end END] -o OPTIONS [OPTIONS ...] [-r]
[-p] [-v] [--path]
Load symbol data.
optional arguments:
-h, --help show this help message and exit
-s SYMBOLS [SYMBOLS ...], --symbols SYMBOLS [SYMBOLS ...]
symbol(s)
-y SCREENER, --screener SCREENER
name of Yahoo screener
-l LIMIT, --limit LIMIT
take the first l symbols
--start START start date of data
--end END end date of data
-o OPTIONS [OPTIONS ...], --options OPTIONS [OPTIONS ...]
indices of data_options in params.py
-r, --refresh refresh the data
-p, --print print the data
-v, --verbose enable logging
--path print the data path
```
| Indicators |
| ---------------------------------------------- |
| daily() |
| daily_adj() |
| sma(period=30) |
| ema(period=20) |
| macd(fast=12, slow=26, signal=9) |
| stoch(fastk=5, slowk=3, slowd=3, kma=0, dma=0) |
| rsi(period=14) |
| adx(period=14) |
| cci(period=14) |
| aroon(period=14) |
| bbands(period=14, ndev=2, ma=0) |
| ad() |
| obv() |
Example
```
python symbol.py -s AAPL -o sma
```