Algorithmic Trading Analysis On Hsbc Plc Stock

Executive Summary

In this paper, we effort to create backtesting with discussion on a company stock HSBC plc and prepare a report for the new hedge fund manager with 2 types of the technical indicators. The first indicators that I used for my trading strategies are moving average and the second for backtesting indicators are stochastic occisilators. The objectives are to mainly improve the indicators used on trading ideas and the historical data. Algorithmic Trading is a one way of investing strategy uses by the investors by providing future expectations using the past or historical data. It commonly uses in finance, mathematics and computer sciences. It processes using the algorithm to generate and execute orders on trading and financial instruments. Backtesting is a process which rational expectations model in algorithmic trading. Moreover, backtesting is highly reliable strategy, investment process. This study mentions the models to be selected for this paper.

Backtesting is a trading strategy based on a historical data. It plays a important role whether the strategy would work or not in trading. In backtesting strategy to predict the future pricing and period it can simulate from the past data to show the usefulness and effectiveness. Traders apply this backtesting technique believes from the past performance of a company stock like HSBC plc would indicates the future results and it also popular strategy uses by traders because it quite straightforward method.

1.1 Advantages of Backtesting

Backtesting strategies is utilized to test the specialized investigation of different techniques to be connected in trading. By utilizing backtesting a trader can test the usefulness of a given methodology and would thus be able to check whether comparable outcomes would have been accomplished as accomplished before. When you have the past outcomes and results accomplished subsequent to backtesting, you can think about and decide if the technique has prescient esteem or not. It is exceptionally basic among technical trader and the most majority of the exchanging is done on Computer. The digitalization time has made the work simpler and less confused.

1.2 Disadvantages of Backtesting

Past results may not apply to future

Since backtesting depends on past data, the outcomes accomplished in the past may not really remain constant later on as well. The trading market is unstable, unpredictable and can change whenever which can influence on volatile execution of results determined performance of backtesting.

The results are not exact

In a market, there are numerous factors influencing the procedure of backtesting. Utilization of various spreads by various traders may modify the outcomes. The unpredictability of economic situations additionally influences the outcomes in light of the fact that even after utilizing same procedure in two diverse market situations will create distinctive outcomes. Another factor influencing the result of applying backtesting is whether it is performed by a human himself or is done algorithmically. Utilizing algorithmic technique will create less mistakes when contrasted with the testing performed by human.

The strategy applied primarily used for this backtesting are Simple Moving Average (SMA) indicator. Using moving averages has the benefits of smoothing out the trends. SMA is the most common indicator used by traders in decades through the years. It is created by computing the average mean price of the share or security. It is common to create a moving average from Open, High, and Low data points, although most of the moving averages are formed using the closing price. When a shorter-term simple moving average cross above a longer-term average it is a buy signal, is a basic known since an uptrend is expected. Opposite, when a long-term average cross above a short-term average it is a sell signal, and a short position can be opened, since a downward trend is expected (C. Toms, 2011). Moving average is highly popular for being easy to use in technical analysis. One of the limitations of the moving average is because the moving average is a lag indicator, and sometimes the signal comes later (Tveiten and Glendrange, 2016).

How to make profits on Simple Moving Average Indicator? Often hear traders talks about moving average of 50 and 200. These are commonly used to observe the moving average. MA 50 and MA 200 means calculated closing prices from previous days respectively. To know more effective SMA for short term trading specific use are 7,14,50 show better and detail cross. Moving on to making profits, price crosses MA 50 from bottom up we buy and the price might continue rise. When MA 50 crossed down we sell and the price fall and we make some profits out of it.

I have chosen HSBC plc stock on daily dataset required from Yahoo Finance to run the daily data on Jupyter. I used the dates and adj close price to see the data framework on my work. In this data we can see the graphs on Yahoo Finance that when the announcement of dividend spit happens, the share price of HSBC dropped because the company have to pay the dividend to its shareholder clients.

I ran these data through Jupyter, a algorithm simulator where practical shows the data and insert my personal indicator to see on the graphs using my strategies. It also required to collect the data from Yahoo Finance on HSBC stock from January to December 2018 of last 12 months. I proceed to run the backtest on my stock that use an initial capital of $10 million based on the report. For each trade I put capital risk at 20% and the results would be promising because I don’t trade all in one basket and tend to lose all the investment.

Further backtesting usually wasn’t enough because our data doing is just 12 months. Based on my understanding backtesting usually required long term data more than 3 years record to proof it work better on market prices to hit. Even though, its 12 months data we still can follow the right rules and presentence to trade in stocks. Moreover, the codes can be view and modify on your wish to see the movement of market volatility and reactions.