"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:

install your requirements

set up your database.. here  are some sample DB configs (pleace in `pypolo/local_settings.py`):

run migration commands

and then install the system crontab

... 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

### Classifier Backtester

### Trade Bot

## Other Administration Commands

These should ideally be scheduled via crontab:

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