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miti0 edited this page Aug 28, 2017 · 2 revisions

AI

Implementation details for using AI in mosquito.

The implementation should handle (at least try to handle as much as possible) following challenges:

Goal

The goal of the AI is to predict following:

  • Predict the next price for each of required currency
  • Predict the value of currencies in the next +15, +30 and +60 minutes
  • Predict which of the stocks will move up by more than x% in the next +5, +30 and +60 minutes
  • Predict which stocks will go up/down by 2x% in the next n minutes while not going down/up by more than x% in that time

Logic

AI implementation in mosquito could be divided in following modules:

Dataset for training

  1. Generate dataset which will contain historical data and calculated features (own section below) for all crypto-currencies. Each dataset will contain also price that was +15, +30 and +60 minutes from the state (this can be used as output Y).
  2. During real-time prediction, new dataset will be added to existing model and model will be re-trained with additional dataset.

Optional: To speed-up the model we could see which one of input features directly correlate with output prices and remove the one that does not.

Input Features

Below is a list of features that are used/implemented in AI and as well candidates, which are currently used in different AI algo predictions mentioned in references.

Implemented

Candidates

Below is a list of features/indicators which have been successfully used in projects lets-write-a-cryptocurrency-bot-part-2 by Joel Degan and Crypto Alert Project - Algorithm

  • Elder ray — Bulls/Bears Power
  • High-Low Index
  • Williams R%
  • Ultimate Oscillator
  • Price Rate Of Change
  • Stochastic RSI
  • Average True Range (ATR)
  • The percentage change in buy volume for the past 15, 30, and 60 minutes compared to the average buy volume in the past 24 hours
  • Following values are computed repeatedly using 15-min, 30-min, 60-min, and 120-min candle stick data (OHLC)
  • The slope of buy volume in the past 4 periods
  • The slope of buy volume in the past 96 periods
  • Stoch RSI Fast K value
  • Stoch RSI Slow D value
  • The moving average of Stoch RSI Fast K
  • The slope of Stoch RSI Fast K in the past 4 period
  • Chaikin Money Flow Index
  • ADX value
  • The slope of ADX in the past 4 periods
  • The sign of SQZ Momentum Index (0:negative, 1:positive)
  • The slope of SQZ momentum index in the past 4 periods
  • The sign of SMI (1: bigger than signal, 0: smaller than signal)
  • The value of SMI
  • The sign of MACD(1: bigger than signal, 0: smaller than signal)
  • The value of MACD
  • On Balance Volume value
  • The slope of OBV in the past 4 periods
  • Ichi cloud indicators (lagspan, baseline, conversion line, Span A, and Span B)
  • Aroon indicator value
  • Slope of Aroon indicator
  • Commodity Channel Index
  • Chaikin Volatility Index
  • The sign of KST (1: bigger than signal, 0: smaller than signal)
  • The slope of KST in the past 4 periods
  • The sign of TDI indices
  • Total volume
  • Buy volume in the past 15, 30, and 50 minutes
  • Percentage change in price in the last 15, 30, 60, and 120 minutes.
  • The total amount of buy orders for the first 50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 buy orders in the tradebook
  • The total amount of sell orders for the first 50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 sell orders in the tradebook
  • Total buy orders in the tradebook
  • Total sell orders in the tradebook
  • Buy Order/Sell Order Ratio for the first 50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 orders in the tradebook
  • Buy Order/Sell Order Ratio for the whole tradebook
  • Number of buy and sell walls in the tradebook (I have my own definition of this)
  • Spread of tradebook
  • Weighted and unweighted midprice in the tradebook

References

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