predictBO

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Predicting Binary Option in Highlow Australia's demotrade using tensorflow (or other machine learning algorithms). This project is very incomplete. Please help!

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// repository documentation

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README
=====

# BETA VERSION IS COMPLETED!
# Current AI in the master node achieves the win rate of 65% in the backtesting on 2017 data!

# PLEASE HELP! PLEASE CONTRIBUTE!

# Warranty
There is no guarantee that these codes works. 
I have no responsibility on any of the consequences arising from modifying,running, and distributing this code.

# Warning

There is nothing amazing happening right now. It is under development, and there is no actual profits or real-time signaling properties
so far. It will happen.

There is no GUI because I have no idea how to do that.

This is not for beginners because I'm not expert enough to make it a beginner friendly... I'm so sorry...

# Dependencies
It requires `python 2.7` and packages that can be installed with `pip` (I'll make an official list). 
I'm a python noob, so I don't know when it will be. It would be nice if I can make some `setup.py`.

# Progress
If you backtest using the AI in Master node, you can see that the winrate is around 65% (plus or minus 3%)!

# TO DO
+ 日本語のドキュメンテーションの作成
+ ~~Implimentation of RMSPropGraves~~ This will never happen.

# Note

Please report any issues or questions on `issues` tab, so that I can ask all the questions at once, 
and your question might already be answered there.

# How to use

## Shaping data

Go to

`http://www.histdata.com/download-free-forex-historical-data/?/ninjatrader/tick-last-quotes/usdjpy/`

Download the zip files for all the month from January of 2014 up to now, extract, and put the csv files in

`./data/tickdata/rawdata/`

Go in to the folder `shapedata` run

`python truncatenight.py`
and 
`python shapetwosec.py`

This will generate a csv files with all the USD/JPY data in an interval of 1 second
(note that the linear extrapolation is taken for missing data points).

## Making AI
Go to tensorflow folder,
and run `makeAI-tf.py`. make sure to turn `cont_learn` to `= False` at line `23` if you are running it for the first time.

Once `saved!` is displayed on a terminal (the command window), run `backtest.py` and see how it goes.
tweak `checkQ` to be `= True` to see how Q-values changes over transactions as a graph.

you need to have this trained at least 100,000 times to see a good progress (still not enough, I think).

## Restart from the saved AI
Go to tensorflow folder,
and run `makeAI-tf.py`, but this time make sure to turn `cont_learn` to `= True` at line `23`
if you want to continue from the AI that is made previously.

## Backtesting

Backtest can be done using `tensorflow/backtest.py`. Just run it and it should output how much you have profitted 
if you traded in whatever years you specified.

**The key to the success is a corrrect Qthresh value! right now it is 1.45,
but if you train further you might want to increase it!**

## Trying it on demotrade

You can run `demotrade_tf.py` to test the AI on the real trading website (it is a demo trade from high-low Australia).

Enjoy.

### sklearn version is obsolete. 
run 
`make-AI-skl.py` this will create a base AI
then run
`prob_clas.py` this takes a probability distribution of the base AI and make further predictions.
then run
`backtest.py` to conduct backtesting.

## Contact
If you have any questions or bugs, either submit in the issues tab or directly contact me via e-mail
(t.hashizume.glasgow@gmail.com).

predictBO

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