pytrader is a cryptocurrency trading robot that uses machine learning to predict price movements at confidence intervals, and sometimes execute trades. It is programmed to work on the poloniex.com cryptocurrency platform.
I (@owocki) built this as a side project in January / February 2016, as a practical means of getting some experience with machine learning, quantitative finance, and of course hopefully making some profit ;).
3/26/2016 - My test portfolio was initialized with a 1 BTC deposit, and after 2 months and 23,413 trades, exited with 0.955 BTC. The system paid 2.486 BTC in fees to poloniex. CALL TO ACTION -- Get this trader to profitability. A strategy is being fleshed out here.
3/27/2016 - Sign up for the pytrader slack channel here.
pytrader uses pybrain and sklearn to make trade ( buy/sell/hold decisions ), and then acts upon them.
Supported classifiers are as follows:
"Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis",
"Quadratic Discriminant Analysis"]```
Here's an example of a *Decision Tree* classifier being used to make a buy (blue), sell (red), or hold(green) decision on the [BTC_ETH pair](https://www.poloniex.com/exchange#btc_eth).
<img src='http://bits.owocki.com/1l3K0W3P1g0A/211224.png' />
and here's a *Naive Bayes* decision tree for the [USDT_BTC pair](https://www.poloniex.com/exchange#usdt_btc)
<img src=http://bits.owocki.com/3f2E38052p0J/238365.png />
On both graphs, the x axis is a recent price movement, and the y axis is a previous price movement, the length of which is determined by a parameter called *granularity*. These graphs show only the last two price movements. The graphing library used is constrained by two dimensional space, but you could generate a classifier that acts upon *n* pricemovements ( *n* dimensional space ).
There are many many different parameters one could use to train a *ClassifierTest*. This problem space is enumerated by the management command *predict_many_sk.py*. For each permutation of parameters, a _percent_correct_ value is generated against actual price movement data. Using this brute force methodology, we are able to discover which classifiers are up for the job of trading.
By testing and tuning various parameters to to the *ClassifierTest*, I was able to consistently predict buy/sell/hold movements between 55-65% of the time, depending upon the currency pair and parameters to the test.
### 2. pybrain neural networks
In addition to using sklearn Classifiers, Pybrain Supervised Learning tools were used to predict price movement. This is represented in the data model as a *PredictionTest*, and the problem space is enumerated in *predict_many_v2.py*. By testing and tuning various parameters in the pybrain NN, I was able to consistently predict directional price movements around 55% of the time.
### Database model
<img src='http://bits.owocki.com/373h1z2l2V3x/download.png' />
## Administration & Optimization
Administration of this tool is primarily done through the django admin.
There's a series of graphs in the admin that show trades, and portfolio profitability over time.
<img src='http://bits.owocki.com/3q2M3u0i3L2g/Image%202016-03-26%20at%209.22.23%20AM.png' />
<img src='http://bits.owocki.com/2a1M292b1E3X/Image%202016-03-26%20at%209.21.16%20AM.png' />
... and allow the graphical debugging of trade decisions ...
<img src='http://bits.owocki.com/0N2z260s0e3p/Image%202016-03-26%20at%209.30.04%20AM.png' />
... and allow the tuning of `PredictionTests` and `ClassifierTests` ...
<img src='http://bits.owocki.com/0m3e1i0u0e0a/Image%202016-03-26%20at%209.22.55%20AM.png' />
by each of the native pybrain (Prediction Test) and sklearn (ClassiferTests) parameters ..
<img src='http://bits.owocki.com/3D3k441p441Q/Image%202016-03-26%20at%209.23.37%20AM.png' />
Once a NN or classifier is found that is better than what is being used, *trade.py* is updated with the most profitable configurations.
self.predictor_configs = [
{'type' : 'nn',
'name' : 'ETH / 5',
'symbol': 'BTC_ETH',
'weight' : 0.1,
'granularity' : granularity,
'datasetinputs': 5},
{'type' : 'nn',
'name' : 'ETH / 5',
'symbol': 'BTC_ETH',
'weight' : 0.1,
'granularity' : granularity,
'datasetinputs': 4},
{'type' : 'classifier',
'symbol': 'USDT_BTC',
'name' : 'AdaBoost',
'weight' : 0.1,
'granularity' : granularity,
'datasetinputs' : 2,
'minutes_back': 1000},
{'type' : 'classifier',
'symbol': 'USDT_BTC',
'name' : 'Naive Bayes',
'weight' : 0.1,
'granularity' : granularity,
'datasetinputs' : 2,
'minutes_back': 1000},
{'type' : 'classifier',
'symbol': 'BTC_ETH',
'name' : 'Naive Bayes',
'weight' : 2,
'granularity' : granularity * 3,
'datasetinputs' : 2,
'minutes_back': 1000},
]
### trade.py
*trade.py* is the system's always-running trading engine. At a high level, it creates & trains ClassifierTests and PredictionTests based upon the most profitable indicators. Once those Tests are trained, it runs a loop that makes trades based upon them if a certain confidence threshold is reached amongst its `self.predictor_configs` .
## Why open source this?
Although I am able to predict price movements with some degree of accuracy that beats random, I was never able to generate a robot that traded profitably *after fees*. [Especially after poloniex changed their fee structure](https://poloniex.com/fees/)
My test portfolio was initialized with a 1 BTC deposit, and after 2 months and 23,413 trades, exited with 0.955 BTC. The system paid 2.486 BTC in fees to poloniex.
The code is not perfect. This was a pre-product/market-fit side project. Please feel free to open an _Issue_ if you do not understand something. CALL TO ACTION -- Get this trader to profitability. [A strategy is being fleshed out here](https://github.com/owocki/pytrader/issues/1).
## Deployment
After you've cloned the repo, you'll want to create a *pypolo/local_settings.py* file with the following information in it:
import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(file)))
API_KEY = "<POLO_API_KEY>" API_SECRET = "<POLO_SECRET>"
DEBUG_APPS = []
this defines whether trade.py will actually submit trades to the poloniex API. setting to False is useful for testing
MAKE_TRADES = True
the following 3 lines are needed only if you want to be alerted of fail cases (when the trader is not running, etc)
ALERT_EMAIL = '<your_email>' SMTP_USERNAME = '<smtp_user>' SMTP_PASSWORD = '<smtp_pass>'
LOG_FILE = '/var/log/django.log'
install your requirements
pip install -r requirements.txt
set up your database.. here are some sample DB configs (pleace in `pypolo/local_settings.py`):
#postgres
DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', # Add 'postgresql_psycopg2', 'mysql', 'sqlite3' or 'oracle'. 'NAME': 'trader', # Or path to database file if using sqlite3. # The following settings are not used with sqlite3: 'USER': 'trader', 'PASSWORD': '', 'HOST': '127.0.0.1', # '127.0.0.1', # Empty for localhost through domain sockets or '127.0.0.1' for localhost through TCP. 'PORT': '', # '5124', # Set to empty string for default. 'ATOMIC_REQUESTS': True, }, }
#sqllite
import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(file)))
_DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } }
run migration commands
./manage.py syncdb ./manage.py migrate
and then install the system crontab
crontab scripts/crontab.txt
... and your system is installed.
Once enough `Price` objects are stored in the database, you'll be able to begin training your NN / classifiers. (see example command `./manage.py pull_prices` below or [download a seed database of prices here](https://github.com/owocki/pytrader/issues/2)).
## Important Administration Commands
### Neural Network Backtester
./manage.py predict_many_v2
### Classifier Backtester
./manage.py predict_many_sk
### Trade Bot
./manage.py trade
## Other Administration Commands
These should ideally be scheduled via crontab:
./manage.py pull_deposits #checks API for any new deposits ./manage.py pull_balance #records your balance in USD, BTC, native coin. ./manage.py scheduled_trades # executes any scheduled trades ./manage.py pull_prices #pulls price data from exchanges
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