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Ful notes and resources at ocdevel.com/mlg/26
NOTE. This episode is no longer relevant, and tforce_btc_trader no longer maintained. The current podcast project is Gnothi.
Episode Overview
TForce BTC Trader
- Project: Trading Crypto
- Special: Intuitively highlights decisions: hypers, supervised v reinforcement, LSTM v CNN
- Crypto (v stock)
- Bitcoin, Ethereum, Litecoin, Ripple
- Many benefits (immutable permenant distributed ledger; security; low fees; international; etc)
- For our purposes: popular, volatile, singular
- Singular like Forex vs Stock (instruments)
- Trading basics
- Day, swing, investing
- Patterns (technical analysis, vs fundamentals)
- OHLCV / Candles
- Indicators
- Exchanges & Arbitrage (GDAX, Krakken)
- Good because highlights lots
- LSTM v CNN
- Supervised v Reinforcement
- Obvious net architectures (indicators, time-series, tanh v relu)
Episode Summary
The project "Bitcoin Trader" involves developing a Bitcoin trading bot using machine learning to capitalize on the hot topic of cryptocurrency and its potential profitability. The project will serve as a medium to delve into complex machine learning engineering topics, such as hyperparameter selection and reinforcement learning, over subsequent episodes.
Cryptocurrency, specifically Bitcoin, is used for its universal and decentralized nature, akin to a digital, secure, and democratic financial instrument like the US dollar. Bitcoin mining involves running complex calculations to manage the currency's existence, similar to a distributed Federal Reserve system, with transactions recorded on a secure and permanent ledger known as the blockchain.
The flexibility of cryptocurrency trading allows for machine learning applications across unsupervised, supervised, and reinforcement learning paradigms. This project will focus on using models such as LSTM recurrent neural networks and convolutional neural networks, highlighting Bitcoin’s unique capacity to illustrate machine learning concept decisions like network architecture.
Trading differs from investing by focusing on profit from price fluctuations rather than a belief in long-term value increase. It involves understanding patterns in price actions to buy low and sell high. Different types of trading include day trading, which involves daily buying and selling, and swing trading, which spans longer periods.
Trading decisions rely on patterns identified in price graphs, using time series data. Data representation through candlesticks (OHLCV: open-high-low-close-volume), coupled with indicators like moving averages and RSI, provide multiple input features for machine learning models, enhancing prediction accuracy.
Exchanges like GDAX and Kraken serve as platforms for converting traditional currencies into cryptocurrencies. The efficient market hypothesis suggests that the value of an instrument is fairly priced based on the collective analysis of market participants. Differences in exchange prices can provide opportunities for arbitrage, further fueling trading strategies.
The project code, currently using deep reinforcement learning via tensor force, employs convolutional neural networks over LSTM to adapt to Bitcoin trading's intricacies. The project will be available at ocdevel.com for community engagement, with future episodes tackling hyperparameter selection and deep reinforcement learning techniques.
