2 09, 2021

New Additions to the PredictNow.ai Factor Zoo

By Akshay Nautiyal and Ernest Chan This has been a summer of feature engineering for PredictNow.ai. First, we launched the US stock cross-sectional features and the time-series market-wide features. Most recently,  we launched the features based on options activities, ETFs, futures, and macroeconomic indicators. In total, we are now offering 616 ready-made features to our subscribers. NOPE - Net options pricing effect - is a normalized measure of the net delta imbalance between the [...]

14 07, 2021

Metalabeling and the duality between cross-sectional and time-series factors

Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in the latter the factors must be from a prior period.  There are generally two types of factors: cross-sectional vs time-series. If you are modeling stock returns, cross-sectional factors are variables [...]

4 06, 2021

Introducing: Pre-engineered Stock Fundamental Features at Predictnow.ai

Predictnow.ai is pleased to announce the launch of our new pre-engineered stock fundamental features. These features are ratios and indicators that have been constructed from the quarterly and annual financial statements of public companies. In the US, these are the 10-Q and 10-K filings to the SEC. Multiple independent studies have shown that merging fundamental and technical features can predict returns more accurately than using technical features alone. (For example, Cao and You, 2020.) [...]

13 05, 2021

Predicting Profitability: Introducing PredictNow.Ai a Live Machine Learning Tool to Alpaca

What is PredictNow.Ai?  PredictNow.ai is a no-code machine learning SaaS-based in Toronto, Canada that is primarily focused on helping traders apply machine learning to their investment strategies in order to predict the profitability of their next trade. PredictNow.ai can calculate the most important input variables groups (also known as “clusters”) that empower a user’s prediction and only incorporate those in the prediction decision. Users have to train a machine learning model based on their historical [...]

1 04, 2021

Conditional Parameter Optimization: Adapting Parameters to Changing Market Regimes

Every trader knows that there are market regimes that are favorable to their strategies, and other regimes that are not. Some regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc. These regimes affect many strategies and portfolios (unless they are market-neutral or volatility-neutral portfolios) and are readily observable and identifiable (but perhaps not predictable). Other regimes are more subtle, and may only affect your specific strategy. Regimes may change every day, [...]

20 01, 2021

The Amazing Efficacy of Cluster-based Feature Selection

One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? What's the intuition behind a certain ML trading strategy? How would you explain a major drawdown? This lack of "interpretability" is not just a problem for financial ML, it is a prevalent issue in applying ML to any domain. If you don’t understand the [...]

6 08, 2020

What is the Probability of Profit of your Next Trade? (Introducing PredictNow.Ai)

What is the probability of profit of your next trade? You would think every trader can answer this simple question. Say you look at your historical trades (live or backtest) and count the winners and losers, and come up with a percentage of winning trades, say 60%. Is the probability of profit of your next trade 0.6? This might be a good initial estimate, but it is also a completely useless number. Let me [...]

5 03, 2020

Why Does our Tail Reaper Program Work in Times of Market Turmoil?

I generally don't like to write about our investment programs here, since the good folks at the National Futures Association would then have to review my blog posts during their regular audits/examinations of our CPO/CTA. But given the extraordinary market condition we are experiencing, our kind cap intro broker urged me to do so. Hopefully there is enough financial insights here to benefit those who do not wish to invest with us. As the name [...]

9 12, 2019

US Nonfarm Employment Prediction Using RIWI Corp. Alternative Data

Introduction The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of the classroom to their desks on a Friday morning just before 8:30am EST.) Naturally, there were many efforts in the past trying to predict this [...]

4 12, 2019

Experiments with GANs for Simulating Returns (Guest post)

By Akshay Nautiyal, Quantinsti   Simulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against empirical observations from stock, bond and other financial time-series data. (See Chan and Ng, 2017 and Lopez de Prado, 2018.)  Some of the stylised facts of return distributions are as follows: The tails of an empirical return distribution are always thick, indicating lucky gains and enormous losses are [...]