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targets are reached based. Sentiment Analysis Crypto Twitter Trading Strategy Barbie Bot. First, the LM and H4 scores were imported, and total scores of the following variables for each article were calculated: lmpos: positive forex italian LM scores lmneg: negative LM scores lm: total LM scores for each article lmpos - lmneg h4pos: positive H4 scores h4neg: negative. Find File, clone or download. Next was the effect of LM polarity scores on returns 1-3 days later. Sentiment Data The analysis was conducted using Stata.
Libraries: Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras. Codebase and License, heres the full github repo for this project. It's not a very elegant implementation but it gets the job done. The Bitcoin Barbie Bot Crypto Trading Bot is an attempt to codify the crypto trading strategy described by @BarbieBuysDips in this tweet: Essentially, the strategy described by @BarbieBuysDips describes how to stay away from Pump Dump schemes, while profiting from their predictable behavior. Apply(lambda row: t_score(row'H4Tokens axis 1) lm_scores lm_name(columns'Negative 'LMNeg 'Polarity 'LMPol 'Positive LMPos 'Subjectivity LMSub inplaceTrue) h4_scores h4_name(columns'Negative 'H4Neg 'Polarity 'H4Pol 'Positive H4Pos 'Subjectivity H4Sub inplaceTrue) #Merging DataFrames into one for Analysis all_data ncat(adf, lm_scores, h4_scores, axis1) all_v Bitcoin Prices The Bitcoin market data is simply. Granger causality tests across 1-3 lags were conducted to see how lagged LM polarity scores and BTC Returns affected LM polarity scores). Join GitHub today, gitHub is home to over 31 million developers working together to host and review code, manage projects, and build software together. If candle decreases 2 (or more) in specified time period (5 minutes, 1 hour, 1 day Purchase quantity of coin.
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