Natural Resources as Stock Market Trouble Alarm
Stock markets shocks are notoriously difficult to predict. In general, periods of high volatility can lead to both large market gains and...
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H-Barrio
- Jun 15, 2021
Storing Map Tiles for Offline Use: HTML to JPG in Jupyter Notebooks
To finish exploring the possible uses for geographic source data for the obtention of intelligence (covered in this post and this post),...
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H-Barrio
- Jun 8, 2021
Mapping GEO Data with Folium
Map generation from geographic data.
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H-Barrio
- Jun 1, 2021
Primary Data Sources: Geographical Data from WFS
Data is the new oil. Data is the new gold. The mithril of Moria. These phrases are used to emphasize the value of data in the current...
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H-Barrio
- May 4, 2021
Correlation Index at the Top of the SPY
SPY correlation index for the recent past. Can stock movements be predicted by the returns correlation of the most capitalized companies?
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H-Barrio
- Apr 13, 2021
Interlude: Automated UML Model with Python
This is an interlude to our usual topics. This post will be one level inside quantitative model development, one level below using a...
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H-Barrio
- Apr 6, 2021
CUSUM Event Identification: Not Just for Sampling
Cumulative sum filtering is a signal sampling method presented in Marcos Lopez de Prado. 2018. Advances in Financial Machine Learning...
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H-Barrio
- Mar 30, 2021
Entropy Based Label Selection
Now we have a tool that will flexibly obtain labels for our hypothetical trading positions, all of them inside a certain space of future...
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H-Barrio
- Mar 23, 2021
DataFrame-Ready Implementation for Triple Barrier Labels
We will continue adapting the methods presented in Marcos Lopez de Prado. 2018. Advances in Financial Machine Learning (1st. ed.). Wiley...
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H-Barrio
- Mar 16, 2021
Stock Price Fractional Differentiation: Best Fraction Finder
This post extends the code for our interpretation of the fractional differentiation calculation methods presented in Marcos Lopez de...
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H-Barrio
- Mar 9, 2021
A DataFrame-Ready Implementation for Standard Fractional Differentiation
If there is momentum, take advantage of it. Our previous posts on fractional differentiation have surged in visits in the past weeks, so...
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H-Barrio
- Mar 8, 2021
Tampering with Universes: Correction and Improvement
In our previous post regarding a flexible universe selection, we presented a more flexible universe selection model. The results we...
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H-Barrio
- Mar 2, 2021
Tampering with the Universes
Now that we have this very flexible universe selection model, we can easily tamper with it and experiment with multiple strategies...
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H-Barrio
- Feb 23, 2021
A Flexible Universe Selection Model
The team at Quantconnect has created some handy tools for their Lean engine; their QC500 universe selection model allows for selecting a...
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H-Barrio
- Feb 16, 2021
Pattern Recognition in Sector ETFs: Industry Return Predictability - Machine Learning Approach
Our last publication regarding pattern recognition using sectorial ETFs did not yield outstanding results. The dependencies among these...
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H-Barrio
- Feb 9, 2021
Moving Averages as Resistances and Supports
In this publication, we will walk through the implementation of the idea of using the "historically best" moving average window value as...
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H-Barrio
- Feb 2, 2021
Performance of Moving Average as Price Supports
Moving averages (abbreviated as MA usually) are venerable tools in the field of stock price technical analysis. The diagram that possibly...
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H-Barrio
- Jan 26, 2021
Robust Feedback Control for Bitcoin Holdings
Cryptocurrencies have soared in price relative to the US Dollar and Euro in the past few weeks drawing additional attention from general...
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H-Barrio
- Jan 19, 2021
Why Pairs Trading Might Not Work: Rolling Cointegration Tests
Pairs trading is the dean, the eldest and better known of modern quantitative trading models. It is the pinnacle of statistical arbitrage...
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H-Barrio
- Jan 12, 2021
Pattern Recognition in Sectorial ETFs: (Highly) Automated Model
In our december publications we went over the development of an automated system that could determine which values (factors) had the...
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