This is a summary of links featured on Quantocracy on Monday, 07/22/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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How to build a Bitcoin Sentiment Analysis Strategy [Augmento]TL;DR: We built a profitable Bitcoin sentiment strategy yielding 2400% returns over 24 months. Adding trading fees made the strategy more realistic while finding optimal sentiment combinations and window sizes increased returns dramatically. In the previous article, we described how to build a strategy based on Augmento Bullish and Bearish Bitcoin sentiment, and backtested it on Bitmex XBTUSD. The
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TAA and Transaction Costs [Allocate Smartly]New to Tactical Asset Allocation? Learn more: What is TAA? There are two hard costs that investors must consider when comparing a tactical asset allocation strategy to conventional buy & hold: (1) increased tax liability (if trading in a taxable account), and (2) increased trading costs (transaction costs and slippage). Because we track so many published models (50 and counting) were in a
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Stocks Don’t Do So Hot – Most equities don’t beat 1m Treasury bills (h/t @thodoha) [Mark Rzepczynski]Stocks are risky investments. Let's be very clear, stocks are risky with positive skew. Of course, everyone knows that but some data published about two years really drove that home. (See my earlier post "Most stocks are losers – Median and skew tell an important story" about the paper "Do stocks Outperform Treasury Bills" by Hendrik Bessembinder)That path-breaking work
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Time Series Decomposition & Prediction in Python [Python For Finance]In this article I wanted to concentrate on some basic time series analysis, and on efforts to see if there is any simple way we can improve our prediction skills and abilities in order to produce more accurate results. When considering most financial asset price time series you would be forgiven for concluding that, at various time frames (some longer, some shorter) many, many of the data sets we
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Ensemble Multi-Asset Momentum [Flirting with Models]We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs. With a monthly rebalance cycle, we find substantial timing luck risk. Using the same basic framework, we build a simple ensemble approach, diversifying both process and rebalance timing risk. We find that the virtual strategy-of-strategies is able to
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The relation between value and momentum strategies [SR SV]Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to have gone too far by standard valuation metrics. Second, value stocks carry particularly high market risk or bad beta and thus fare well when