Backtesting is a simple process which helps a trader to evaluate his trading ideas and provides information about how good the trading system performs on the given historical dataset. It talks a lot about the behavior of the trading system, risk involved in trading a particular trading system and lot about trading system performance. Here is a video tutorial with step by step guide on how to perform a simple backtesting using Amibroker.
Backtesting is a process of Testing the trading conditions with respect to the past historical data, evaluating not only the profitability of the system but the underlying risk factor associated with the Trading/Investing Model. Proper Backtesting gives belief and enough confidence to a trader to trade a set of rules. But are the newbie traders really doing proper backtesting?
Backtesting and Optimization to be pretty much essential step in trading strategy development. If the strategy is not performing well in the backtest results we can skip the system and move on to the next one. But if the backtest results are good then one should be extra cautious as most of the times backtesting your own Trading Strategy might give interesting results. However when comes to practical trading the scenario might be completely different and most of the times it results in a poor performance or lower than the expected backtest results.
Quantopian, is a Boston-based algorithmic trading platform and Zipline is a Pythonic algorithmic trading library(Open Source). Zipline is currently used in production as the backtesting engine powering Quantopian.
For a Non Programmers it is really challenging to understand how to backtest future scripts in Amibroker. To solve this issue i had created a simple backtesting template where most of your backtesting settings are eliminated and its quite easier to understand too.
Algorithm trading is a system of trading which facilitates transaction decision making in the financial markets using advanced mathematical tools.Here are the basic steps an algo trader have to go through his/her learning cycle to become a professional Algo Trader.
Rotating inversely correlated assets to select the top performer. We achieved a 2x gain on underlying asset performance tracking the USDINR – NIFTY.
Here is a real life example from Gary Chan sharing the way he started running Algorithmic Pair Trading strategies. Its a pretty old video taken from NYC Algorithmic Trading Meetup. It is a must watch if you are algo trading enthusiast and willing to start your game.
Selecting the right benchmarking technique and index can be critical for truly understanding your strategy success. In this post we benchmark the Sell in May strategy with the S&P 500 to achieve the same return with a lower volatility over the same period.