Trading Bot Specifications

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Elvin Gallagher
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1 Trading Bot Specifications Core Strategy Improved Strategy Trailing Stop Trading Example Volume Analysis with Auto-Encoders News Polarity Detection via Sentiment Analysis Predictive Algorithms

2 Core Strategy Trailing Stop: stop-loss price is not set at a fixed, absolute amount, but it is set at a certain percentage below the market price. Takes advantage of all the increase in price

3 Core Strategy Cryptocurrency selection Select the worst altcoins in terms of major loss in % (last 24 hours) Evaluate several indicators (linear regression, Bollinger bands, PSAR, RSI, Stochastic RSI, etc.) and compute a combined indicator Buy the top k altcoins according to the combined indicator Example: 1 bitcoin, 10 slots (k=10), 0.1 BTC per altcoin. Monitoring and sell Keep holding tokens during all the increasing trend Sell them if they lose more than 5% (Trailing Stop) This prevents selling for minor losses

4 Core Strategy - Trading example The example shows a time series of steem/btc prices (blue line) normalized between 0 and 100, and two meta-indicators I1 and I2 (red and yellow lines). Each point of the blue series represents the closing price for a candlestick of 4 hours. Two grey lines, one at y=30 and one at y=80 allow to detect, respectively, overbought and oversold conditions. Our meta-indicators I1 and I2 produce a buy signal when the currency is recovering from the oversold condition.

5 Improved Strategy - Concepts Auto-Encoders Artificial Neural Networks trained with time series data representing normal conditions. Testing time They return a low reconstruction error if the current time-series belongs to the same distribution as the training data. High reconstruction error Can be used as a signal of incoming abrupt changes in the time series.

6 Improved Strategy - Concepts Ensemble learning Involves a combination of multiple algorithms to improve predictive performances of a model. Bagging: considers the outcome of each algorithm as a vote, then choose the majority vote in case of binary outcome (increase or decrease) or the average of the votes in case of exact price prediction. Meta-learning: Combines different predictions, involving a meta-model called combiner, which takes all the predictions as input.

Improved Strategy - Concepts Sentiment analysis Involves natural language

database where each term is associated with some numerical scores indicating

7 Improved Strategy - Concepts Sentiment analysis Involves natural language processing, text analysis, computational linguistics to identify affective states and subjective information. SentiWordNet: lexical resource for opinion mining derived from the WordNet database where each term is associated with some numerical scores indicating positive and negative sentiment. Strategy: identify the polarity of news to improve the criteria for the selection of cryptocurrencies to buy.

Improved Strategy - Algorithm Precondition Train three Auto-Encoders using time series data of: - (O):Overall capitalization - (B): BTC market cap time series - (A): Altcoins market cap time series

8 Improved Strategy - Algorithm Precondition Train three Auto-Encoders using time series data of: - (O):Overall capitalization - (B): BTC market cap time series - (A): Altcoins market cap time series Check if Altcoins cap is stable (A) Anomaly Detection Cryptocurrency selection Select Altcoins with the highest % loss in the last 24 hours Identify Altcoins which show an inverting trend Combined indicators Increase the score of a specific Altcoin if - There is evidence of positive news Sentiment Analysis - Multiple predictive models show the consensus that it has potential to increase Buy the top k Altcoins with the highest score. Ensemble Learning

9 Improved Strategy - Algorithm Monitoring and sell IF market cap is stable for Auto-Encoder (O) IF Auto-Encoders (B) and (A) detect capitals shifting from Altcoins to BTC Sell the currently k worst performing altcoins ELSE altcoins cap is stable For each cryptocurrency position opened Sell tokens if the price decreases more than 5% with respect to the last peak observed. prevents selling for a minor decrease in price ELSE Sell all for USDT. major loss in overall capitalization has been detected

10 References Trading indicators AI / Machine Learning Forecasting economic time series, Clive William John Granger, Paul Newbold 2014 (Book) Anomaly Detection with Robust Deep Auto-encoders, Chong Zhou, Randy Paffenroth, KDD Ensemble approach for time series analysis in demand forecasting, Akyuz, A. Okay, et al. IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017 Opinion mining and sentiment analysis, Pang, Bo and Lee, Lillian and others, Foundations and Trends in Information Retrieval, 2008

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