Between the excitement of manual trading and the unemotional mind-set of full automated systems, more and more Indian retail traders have been able to find a comfortable middle-ground that fits their timeframes and their risk-taking abilities. Retail trading automation does not necessarily imply giving an algorithm everything. To most participants, it entails eliminating the most emotionally tainted choices in the equation whilst remaining in the strategy making process and continuous control.
Indian traders with technical backgrounds continue to use expert advisors on MetaTrader 4 as the most popular automation tool. An Indian software developer that trades currency pairs on his lunch break may spend his weekends developing and testing an EA to implement his strategy when he cannot be at his desk. That is not passive income in any assured sense, but the capacity to implement a consistent strategy without the disruption of exhaustion, distraction or emotional response to a losing streak. Most traders note that their automated strategies do not outperform their manual trading results on paper, but come with significantly reduced psychological cost.
The culture of backtesting has long since grown within the circles of Indian traders. Where previously participants would use a more or less intuitive approach or even a couple of weeks of observation, a more disciplined cohort will now insist on testing over several years of historical data before committing real capital. Platforms that provide strong backtesting facilities have become popular with this community and forum discussions have moved beyond mere sharing of strategies to more in-depth discussions of the topics of overfitting, sample size and out of sample validation. Five years ago that amount of methodological cognizance would have been considered odd in the retail world.
Copy trading platforms occupy a distinct niche within the automation discussion. They are attractive to traders who desire market exposure but do not have the time to devise independent strategies and the model has had a real following in Indian cities where professionals with disposable income do not have the bandwidth to analyze actively. The risk that experienced traders consistently highlight is that copying the positions of a successful trader without understanding the reasoning behind those positions makes it impossible to know when that strategy has stopped working. A copied account is able to take huge drawdowns before the follower becomes aware that the strategy has completely failed.
Among the more specific automation tools are algorithmic systems based on economic calendar triggers. Other Indian traders have built systems that either change the size of positions or tighten stop-losses as high impact data approaches, eliminating the necessity to watch screens in volatile windows. The CFD trading market is friendly to this type of rules-based risk management since the instruments involved tend to respond predictably enough to scheduled events that pre-programmed responses translate well into live conditions.
Risk automation receives significantly less attention than entry automation. Traders who have put a great deal of effort into the development of entry signals usually consider stop-loss placement and position sizing as secondary concerns, and tend to approach them in an ad hoc fashion. The more disciplined members have dealt with this by automating their risk parameters with the same discipline they use with entries. A trader running CFD trading strategies across multiple instruments simultaneously cannot realistically track the risk profile of each position in real time, and automating trailing stops, maximum daily loss limits, and exposure caps has become less of an option and more of a necessity for serious traders.