Release 1.0
This release provides an API that recommends Buy/Sell of Bitcoin for a given date with intention to maximize profits.
Requirements to BUY (based on Marc Howard's blog ):
- Search terms of “Buy Bitcoin” to “BTC USD” ratio is more than 35%.
- BTC price difference closes more than $80 above the prior day’s close price.
- Get historical transaction data for Bitcoin.
- Get Google Trends data.
- Import data into database.
- Identify buy/sell based on parameters described in blog.
- Use Django to create API.
- Write api docs and publish.
- Switch to SQLite database for easier sharing.
- Use graphical analysis to see how well the current buy/sell recommendations are performing.
- Explore other sources (ie. BitMEX, CCXT).
- Optimize existing parameters (specifically, revise Google Trends and BTC price change thresholds).
- Incorporate OHLCV (open, high, low, close,volume) trends.
- Clone repo to your development environment
git clone https://github.com/lauramayol/crypto_signal.git
- Install virtualenv
pip install virtualenv
- Change directory to project folder
cd crypto_signal
- Start virtualenv
virtualenv --python=/usr/local/bin/python3 env
- Run virtualenv
source env/bin/activate
- Install dependencies using pip
pip3 install -r requirements.txt
- Add Nomics Api Key to environment variables. Nomics
export NOMICS_API_KEY=YOUR_API_KEY
- Run development server
python crypto_signal/manage.py runserver
- Check http://127.0.0.1:8000/ on your browser.
| Location | Endpoint |
|---|---|
| Root path | / |
| Signal | /<simulation_id>/signal |
| Load Bitcoin data | /load/nomics |
| Load trends data | /load/trends |
| Update candles foreign keys | /update/candles |
| Update BUY/SELL signal | /update/<simulation_id>/signal |
| Method | Endpoint | Query | Description | Examples |
|---|---|---|---|---|
GET |
/ |
n/a |
Retrieves the current list of Simulations that can generate a signal. Reference this list to use the correct simulation_id with other methods. | / |
GET |
/<simulation_id>/signal |
?currency=BTC&date=yyyy-mm-dd |
Retrieves the Buy/Sell signal from specified simulation in database for given currency (currently only Bitcoin (BTC) available and historical date (Jan 2013-Oct 2018). | /1/signal?currency=BTC&date=2018-08-15 |
POST |
/load/nomics |
?currency=BTC&start=yyyy-mm-dd&end=yyyy-mm-dd |
Full load of candle (OLHCV metrics) from Nomics.com with given currency and start/end dates (optional). Currently defaulted to daily (1d) intervals and start/end is blank (all-time). | /load/nomics?currency=BTC&start=2018-01-01 |
POST |
/load/trends |
?currency=BTC |
Full load of Google trends Interest Over Time metrics using pytrends library. We are comparing the Google search terms "buy bitcoin" and "BTC USD" Worldwide, and pulling the daily data on 180-day interval starting with today down to 2013. | /load/trends?currency=BTC |
PATCH |
/update/candles |
?currency=BTC |
Updates foreign key relationship of candle to trend model. | /update/candles?currency=BTC |
PATCH |
/update/<simulation_id>/signal |
?currency=BTC |
Updates BUY/SELL signal for each candle based on specified simulation. | /update/1/signal?currency=BTC |
- Issue Tracker: https://github.com/lauramayol/crypto_signal/issues
- Source Code: https://github.com/lauramayol/crypto_signal
If you are having issues, please let me know by posting on Issue Tracker.
Pre-Release
The goal of this project is to provide an API that recommends Buy/Sell/Hold of Bitcoin at any given time to maximize profitability.
Goal: re-create the example from https://hackernoon.com/how-i-created-a-bitcoin-trading-algorithm-with-a-29-return-rate-using-sentiment-analysis-b0db0e777f4
- Get historical transaction data for Bitcoin.
- Get Google Trends data.
- Import data into database.
- Identify buy/sell based on parameters described in blog.
- Use Django to create API.
- Write api docs and publish.
- Switch to SQLite database for easier sharing.
- Use graphical analysis to see how well the current buy/sell recommendations are performing.
- Explore other sources (ie. BitMEX, CCXT).
- Optimize existing parameters (specifically, revise Google Trends and BTC price change thresholds).
- Incorporate OHLCV (open, high, low, close,volume) trends.
- What other variables could play a part in Bitcoin price?
- Get live transactional and order book data.
- Compare various sources for historical data and implement as needed.
- Issue Tracker: https://github.com/lauramayol/crypto_signal/issues
- Source Code: https://github.com/lauramayol/crypto_signal
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