Capitalise is a trading platform that uses NLP technology to take your trading strategy from an idea into a fully automated system. Simply type your trading strategy into the easy-to-use interface in plain English, then watch your strategy run automatically. Automate your trading strategy using natural language processing (NLP) with… Read More
In this research, the authors create an algorithmic trading strategy that attempts to predict the price of Bitcoin in a variety of minute intervals. Three models were used: a simple…
Receive free Forex and CFD Market Data for algorithmic trading in one easy step. Read More
In celebration of 20 years of trading, FXCM is kicking off a worldwide algo trading tour bringing exciting events to our trading communities around the world in order to connect…
Your monthly news & research update for all things quant trading This month, QuantNews looked at how sentiment data can be used for an algo trading strategy. We take you…
Saturday, 23rd March 2019 12:00 - 15:00 GMT
Register now for a quant trading seminar live in Toronto where professional quant traders bring you practical guidance on algorithmic trading. Read More
Traders and market analysts use volume data, which is the amount of buying and selling of an instrument over a given time period, to gauge the strength of an existing…
With only 0.6% of mutual funds outperforming the benchmark index, many individual investors have limited opportunity to gain alpha. The authors set out to discover if these investors can attain…
FXCM offers premium data packages with valuable sentiment, volume and order flow data. In this article we will download a sample of the sentiment data set into a Pandas DataFrame…
Thursday, 21st March 2019 18:00 - 19:00 GMT
We have teamed up with FXCM Education to bring you a live webinar introducing you to the world of automated strategies. In this live session, you will have the opportunity…
Sentiment data shows the buyer-to-seller ratio of an instrument and it can be used to implement a mean reversion algorithm. Read More
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