6 Live Sentiment Analysis Trading Bots using Python

Label data for bullish, bearish, and neutral texts, identify scams and bots, and expand the dataset to improve a state-of-the-art machine learning model.

Install pirnie in google colab and load the bitcoin data csv to create a dataset notebook. Map bullish, neutral, and bearish labels to numbers and prepare bert-ready data.

Predict tweet sentiment with a loaded classifier by preprocessing text, removing colons, and predicting probabilities to assign bullish, neutral, or bearish labels for trading.

Apply a sentiment-based trading strategy that uses the last 300 to 500 tweets to compute an average sentiment and trigger buy or sell signals when thresholds are exceeded.

Implement buy and sell orders in a sentiment-based trading bot, trigger buy signals using a position flag to avoid repeated buys, and execute orders via an API.

Use Python to scrape the fear and greed index and validate a sentiment-driven trading strategy, create a get index function, convert values to float, and print index data for decisions.

Learn to build an NLP sentiment analysis and classification pipeline using a financial news dataset in Python, covering data preprocessing, encoding, labeling, and preparing data for model training and prediction.

Split the data into training and validation sets with a 20% test size, convert text to numeric arrays, and one-hot encode labels for neural network input.

Tokenize text, convert sentences to padded sequences, and prepare fixed input shapes for a neural network using TensorFlow preprocessing and pickle saving for future predictions.

Learn to save and load a trained model, load a pickled tokenized dataset, and run predictions on new phrases as part of building sentiment analysis trading bots.

Preprocess news titles with tokenization and padding, build and use a sentiment model to predict daily sentiment, and integrate these predictions into a cloud-run trading strategy that buys Tesla.

Build a Python script that scrapes news titles, runs sentiment analysis with a model, and creates a sentiment column in a data frame to power a trading strategy.

Sign up with Alpaca Markets and set up a paper trading account to practice algorithmic trading, connect via the alpaca-trade-api, generate keys, and fetch account data before implementing sentiment-based trading.

Develop a sentiment-based trading rule: buy Tesla if negative sentiment from articles exceeds 30, sell if positive sentiment exceeds 30, and transition to a state-of-the-art NLP algorithm for tweets.