Bitcoin Signal

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):

1. Search terms of “Buy Bitcoin” to “BTC USD” ratio is more than 35%.
2. BTC price difference closes more than $80 above the prior day’s close price.

Reference Marc Howard's blog: https://hackernoon.com/how-i-created-a-bitcoin-trading-algorithm-with-a-29-return-rate-using-sentiment-analysis-b0db0e777f4

Features

This release

• Get historical transaction data for Bitcoin.
• Get Google Trends data.
• Import data into database.
• Clean/set up views for analysis.
• Identify buy/sell based on parameters described in article.
• Use Django to create API.
• Write api docs and publish.

Next release

• Switch to SQLite database for easier sharing.
• Use graphical analysis to see how well the current buy/sell recommendations are performing.
• Explore switching to BitMEX source.
• Optimize existing parameters (specifically, revise Google Trends and BTC price change thresholds).
• Incorporate OHLCV (open, high, low, close,volume) trends.

Paths

Location	End Point
Root path	/
Signal	/signal
Load Bitcoin data	/load/nomics
Load trends data	/load/trends
Update candles foreign keys	/update/candles
Update BUY/SELL signal	/update/signal

HTTP request and query methods

Method	End Point	Query	Description	Examples
GET	/signal	?currency=BTC&date=yyyy-mm-dd	Retrieves the Buy/Sell signal from model in database for given currency (currently only Bitcoin (BTC) available and historical date (Jan 2013-Oct 2018).	/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 Interet 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/signal	?currency=BTC	Updates BUY/SELL signal for each candle.	/update/signal?currency=BTC

Contribute

• Issue Tracker: https://github.com/lauramayol/crypto_signal/issues
• Source Code: https://github.com/lauramayol/crypto_signal

Support

If you are having issues, please let me know.

Project Plan: Bitcoin signal

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.

To-do

1.0 release

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.
• Clean/set up views for analysis.
• Identify buy/sell based on parameters described in article.
• Use Django to create API.
• Write api docs and publish.

2.0 release

Goal: optimize parameters

• Switch to SQLite database for easier sharing.
• Use graphical analysis to see how well the current buy/sell recommendations are performing.
• Explore switching to BitMEX source.
• Optimize existing parameters (specifically, remove Google Trends parameter and revise BTC price change threshold of $80).
• Incorporate OHLCV (open, high, low, close,volume) trends.

3.0 release

Goal: live model and optimize the data sources

• 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.

Contribute

• Issue Tracker: https://github.com/lauramayol/crypto_signal/issues
• Source Code: https://github.com/lauramayol/crypto_signal