Getting the newsfeed directly into my favorite charting software is always been interesting to me. If you are a news-hungry trader/investor possibly you may be also thinking in the same front as it increases productivity while trading hours rather than opening a separate portal and scrolling through the newsfeed one by one. It also helps you to have a quick overview of the market before the market starts and the quick market summary post market hours as well.
Here is a simple Amibroker AFL code snippet which tracks the last trade price with a dashed horizontal line at LTP. It could be useful for the people who wants to know where the current market price is trading relative to their support/resistance/some other reference levels.
Do simple strategies really work in Indian Markets? This curiosity arises when one of our Amibroker Mumbai Participant comes up with a simple trading strategy. Hence the tutorial series, Do Simple Strategies Really Works?. This tutorial series explores the space of simple rules, easy to practice, easy to adapt and the to explore if there is any real edge with the simple rules.
while back-testing by default Amibroker provides Profit Table in Compounded percentage terms. However the profit table can be customized according to ones requirement. Rather than doing a month on month back testing and recording the returns manually one can simply custom design the Profit Table to automate his requirement.
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.