PythonQuant tradingRTools of the trade
I’m a bit late to the party with this one, but I was recently introduced to the feather format for working with tabular data. And let me tell you, as far as reading and writing data goes, it’s fast. Really fast. Not only has it provided a decent productivity boost, but the motivation for its
Quant tradingTools of the tradeTrading infrastructure
If you’ve ever delved into the world of retail foreign exchange trading, you’ll have come across the MetaTrader platform. Let’s be clear. The platform has its drawbacks. If you’ve traded “grown-up” markets, some of the features will leave you scratching your head. But one thing’s for sure – MetaTrader provides fast, convenient access to pretty
BacktestingQuant tradingRThink like a trader
In our inaugural Algo Bootcamp, we teamed up with our super-active community of traders and developed a long-only, always-in-the-market strategy for harvesting risk premia. It holds a number of different ETFs, varying their relative weighting on a monthly basis. We’re happy with it. However, the perennial question remains: can we do better? As you might
Quant tradingThink like a traderTrading strategies
This risk premia post is part of a series derived from one of our recent Bootcamps, in which we developed a strategy for harvesting risk premia. We have allocated proprietary capital to the strategy, and many of our members are trading it too. In our Bootcamps we develop trading strategies in collaboration with the Robot
Quant tradingThink like a trader
This is Part 2 in our Practical Statistics for Algo Traders blog series—don’t forget to check out Part 1 if you haven’t already. Even if you’ve never heard of it, the Law of Large Numbers is something that you understand intuitively, and probably employ in one form or another on an almost daily basis. But
Quant tradingRThink like a trader
This is the first in a two-part series. Be sure to read part 2 – Practical Statistics for Algo Traders: The Law of Large Numbers How do you feel when you see the word “statistics”? Maybe you feel that it’s something you should be really good at but aren’t. Maybe the word gives you a
BacktestingFXPythonZorro
One of the ongoing research projects inside the Robot Wealth community involves an FX strategy with some multi-week hold periods. Such a strategy can be significantly impacted by the swap, or the cost of financing the position. These costs change over time, and we decided that for the sake of more accurate simulations, we would
PythonQuant tradingTrading infrastructure
Cryptocompare is a platform providing data and insights on pretty much everything in the crypto-sphere, from market data for cryptocurrencies to comparisons of the various crytpo-exchanges, to recommendations for where to spend your crypto assets. The user-experience is quite pleasant, as you can see from the screenshot of their real-time coin comparison table: As nice
BacktestingQuant tradingThink like a traderTrading strategiesZorro
At Robot Wealth we get more questions than even the most sleep-deprived trader can handle. So whilst we develop the algo equivalent of Siri and brag about how we managed to get 6 hours downtime last night, we thought we’d start a new format of blog posts — answering your most burning questions. Lately our
BacktestingDeep learningFXKerasNeural networksR
Deep Learning for Trading Part 4: Fighting Overfitting is the fourth in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Deep Learning for Trading Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques
BacktestingDeep learningFXKerasNeural networksQuant tradingRZorro
This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals
Deep learningKerasNeural networksRTools of the trade
This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful
Think like a trader
Think like a trader
Think like a trader
CryptoData analysisThink like a trader
Think like a trader