Market Regime Indicators for Algorithmic Trading

I recently came across a few articles about using market regime indicators in one’s algorithmic trading strategies.  Market regimes are trending periods in the market, namely, a bullish market or a bearish market.  Depending on one’s trading style, having a quantitative way of ascertaining whether the market is trending up or down is important and can help manage risk.

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QuantConnect Adds Forex and Equity Tick Data

I’m a bit late to this news, but QuantConnect, the cloud-based algorithmic trading platform, has announced that it has added tick data for the major forex pairs going back to 2007, and tick data for US equities going back to 1998, courtesy of FXCM and QuantQuote.  With a powerful IDE for programming strategies using C#, access to data from Estimize and StockPulse, and the attention of investors looking for emerging hedge fund managers to seed, QuantConnect is a worthy rival to Quantopian.  May the competition continue, to the benefit of the trading public.

(via Finovate)

Equametrics Launches Rizm 1.0 For Algorithmic Trading Without Programming

Equametrics today announced that it was launching Rizm 1.0, a platform for developing algorithmic trading strategies, backtesting them, and trading them live through either Interactive Brokers or FXCM without the need for programming.  The algorithms are instead developed using a visual building-block interface.  Rizm enters an increasingly crowded arena, with other visual algorithmic trading platforms like ProdigioRTS, Visual JForex, and FxPro Quant on the one hand, and cloud-based platforms for programmers like Quantopian and QuantConnect on the other.  Please see Fortune and ForexBrokerz for their respective takes on the launch of Rizm.


How To Identify Algorithmic Trading Strategies

Mike from Quantstart posted a great, comprehensive look at the process and necessary steps in creating an algorithmic trading strategy.  It’s a long article, but it is a great survey of all of the tools you will need to get the job done (you can find many of the tools mentioned in the article, and even more on the Algorithmic Trading and Autotrading Universe page).  Just to summarize the main points, Mike points out that in order to maintain discipline and successfully trade (both on a discretionary and non-discretionary basis), one needs to know one’s own personal preferences.  If you create a strategy or use someone else’s, and it doesn’t match your own preferences, you will be tempted to intervene and override the trading rules set forth by the strategy, which will result in failure.

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