Introducing Hawksight, a simple yet powerful investment analysis platform for data driven investors

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Coming from a data science background, I’ve always believed that being “data-driven” with your investments is important if you want to get returns that can potentially beat the market. This typically involves performing quantitative analysis, and executing trades using algorithmic strategies, that utilize programming languages like Python, and R.

The main point of data-driven investing is that you choose your trades based on strategies that have measurable evidence of actually working in the past, in such a way that you expect them to continue to work in the future.

Historically, this is already being done successfully by “quant hedge funds”…

Use fastquant to easily pull crypto data for your own analysis

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Cryptocurrency (or “crypto” for short) is one of the hottest investments to go for now with stories of people becoming overnight millionaires from buying into Bitcoin early. Since then, quite a few exchanges have opened and even more coins have risen to prominence.

Aside from Bitcoin, another great example is Ethereum, which is now the second largest cryptocurrency platform. …

Use fastquant to easily optimize your trading strategy’s parameters automatically!

The goal of backtesting is to know which areas in the space lead to better outcomes

If you want to consistently earn money with your investments, backtesting is one of the best ways to assess the effectiveness of your trading strategies — of course, assuming that you implement it properly. The idea is that you can experiment with the different parameters for your chosen strategies, and see which combinations give you the best returns for your investment.

However, this can easily start getting tedious as you would have to run hundreds or even thousands of parameter combinations manually.

To solve this problem, we can use fastquant to implement a technique called “grid search”, which basically allows…

Introducing fastquant, a simple backtesting framework for data driven investors

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Ever since I started investing back in college, I was exposed to the different ways of analyzing stocks — technical analysis and fundamental analysis. I’ve even read books and countless articles about these techniques.

In a nutshell, technical analysis argues that you can identify the right time to buy and sell a stock using technical indicators that are based on the stock’s historical price and volume movements. On the other hand, fundamental analysis argues that you can measure the actual intrinsic value of a stock based on the fundamental information found in a company’s financial statements.

Both types of analyses…

Introducing fastquant, a tool for easy access and analysis of Philippine stock data

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As a data scientist with a finance background, I’ve always been fascinated with the idea of making money by trading stocks because it seemed simple. Why? You only needed to know two things to make money — to buy at the lows, and to sell at the highs.

So, how do you know when the stock is going to go up or down? By analyzing the relevant data about the stock (e.g. prices, earnings, and other economic indicators). Theoretically, if you could somehow identify the (predictable) patterns in prices, you should be able to identify the lows at which to…

Yes, and he’s almost as funny as me!

A robot laughing at a friend’s joke (Pinterest)

Tito Joker is a humorous AI that uses state-of-the-art deep learning to tell jokes. His goal is to understand humor well enough to tell jokes that are actually funny.

Why is he named Tito Joker? Because in Filipino, “tito” means “uncle” when translated to English, and in the Philippines, we all have that uncle who says the corniest jokes!

I started when I was in college and still continue up to this day!

My decision to get into data science started way back when I was still in college in early 2015. I actually didn’t plan to become a data scientist originally, but a quant — someone who is essentially a financial analyst that uses advanced math and coding in their functions (e.g. risk management and algorithmic trading); however, a 9-month quant internship made me realize that I wanted to apply these skills to a wider context. A few blog post readings later, I concluded that data science was the field for me.

Coming from a background in Applied Economics, I felt my…

The theory of ML is hard, the application is even harder!

I’ve spent the last few years applying data science in different aspects of business. Some use cases are internal machine learning (ML) tools, analytics reports, data pipelines, prediction APIs, and more recently, end-to-end ML products.

I’ve had my fair share of successful and unsuccessful ML products. There are even reports of ML product horror stories where the developed solutions ended up failing to address the problems they were supposed to solve. To a large extent, the gap can be filled by properly managing ML products to ensure that it ends up being actually useful to users.

Something I quickly learned…

A basic python workflow for predicting the stock price of the Philippine company, Jollibee Food Corp (JFC)

I’ve been into stock trading in the Philippines since I was in college and was immediately convinced that this is a field where forecasting models would be very useful. Why? Because your ability to make money in the stock market is dependent on your ability to predict what the price is going to be in the future.

In this post, I will demonstrate a quick way to forecast the daily closing price of a Philippine stock, Jollibee Foods Corporation (JFC), using the powerful Prophet package on python. Prophet is an open source forecasting tool made by Facebook that speeds up…

Lorenzo Ampil

Co-Founder & CTO @ | Creator of the fastquant python package | AI Products for Finance with #ML, and #NLProc

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