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.
In the last tutorial we explored Kalman filter and how to build kalman filter using pykalman python library. In this section we will be dealing with python com server to integrate Amibroker + Python to compute Kalman Filter and Unscented Kalman Filter Mean Estimation and plot the same in Amibroker.
Here is the first prototype from Marketcalls which demonstrates multi-timeframe based trading system which compares two timeframes (5min and hourly in this case) and takes a trade decision based on both the timeframes. To demonstrate with simple example we used supertrend on 5min timeframe and Hull RSI on the hourly timeframe to filter unwanted trading signals.
Hull ROAR indicator helps in identifying the fastest raising shares and filters it out of the fastest rising shares. Hull ROAR is the brainchild of Alan Hull (author of active investing). ROAR stands for Rate of Annual Return. The rate of annual return is calculated by taking the annual increase in price activity and dividing by the current share price. The result is multiplied by 100 to convert it to a percentage.
Here is the simple AFL code to compute the 10 year rolling returns for any script. Generally Rolling Returns are computed for 3yr, 5yr, 10yr period. Rolling Returns are basically a performance measure of fund/index/stock over a period of time.
Here is the simple prototype for finding first 1 hour cumulative volume for a given script. This helps one to visualize how the volume in the first 1 hour compare to previous trading days.
Prediction Cycle Plugin is a simple and free plugin for amibroker which separates the underlying cycle component from the price and helps you understand the ongoing cycle. Gives a better perspective about the cycles happening the larger timeframes (Daily, Weekly,Monthly) and thereby better decision making.
Sometime back we introduced Lin-Supertrend Live charts for 5min and 10min timeframe. Lin Supertrend is the responsive version of Supertrend V4.0 and the price component in the ATR channel is smoothed with linear regression to make the channel responsive to price movements thereby having tighter trailing stops and responsive change in signals as well.
In the new version of Supertrend thought of removing ATR factor to make the trading strategy independent of volatility factor. It is a simple long/short strategy fits for trading lower timeframes (5min, 10min, 15min) and strategy requires carry-forwarding the open position.
Here is a simple and modified version of Stochastic Momentum oscillator in color coded histogram format. If you understand Stochastic Momentum Oscillator values ranges between +100 to -100. And Generally takes median of High and Low as input. Whereas in the modified version we changed the inputs to median of High, Low and Close. And also the modified version of Stochastic Momentum oscillator has two lookback periods instead of two.
Laguerre PPO Oscillator is just the translated Tradingview Pinescript indicator from theLark’s Laguerre PPO. We very well know that Lauggerre RSI is from the John Ehlers library and Price Percentage Oscillator (PPO) is a classical momentum oscillator. So Laguerre PPO is nothing but a fusion between Laugerre RSI and PPO Oscillator.
One Timeframing is a simple, powerful and popular concept when comes to a market profile trader. One Timeframing generally refers to a market that is trending in one direction.