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Our main focus will be on generating a static forecasting model. We will also check the validity of the forecasting model by computing the mean error. However, before moving on to building the model, we will briefly touch upon some other basic parameters of Time Series like moving average , trends, seasonality, etc.

We can plot the adjusted price against time using the matplotlib library which we have already imported. Using Time Series, we can compute daily returns and plot returns against time. We will also plot the daily returns against time.

Similar to returns, we can calculate and plot the moving average of the adjusted close price. Moving average is a very important metric used widely in technical analysis. For illustration purpose, we will compute 20 days moving average. Since traders deal with loads of historical data , and need to play around and perform analysis, Date-Time Data is important.

These tools are used to prepare the data before doing the required analysis. We will majorly focus on how to deal with dates and frequency of the Time Series. Also, we will discuss indexing, slicing and slicing operations on Time Series.

The below code will execute the same. Of course, depending on the time you are running the code, the output will change. We can compute the difference between two dates using datetime. Let us give a brief note of major data types which are of great use while analysing Time Series. We can convert a datetime format to a string and save it under a string variable, let us see how. As we mentioned about the conversion of a datetime format, the reverse can also be done and a string which represents a date can be converted to datetime data type.

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To understand various operations on a Time Series, let us create a Time Series using random numbers. The elements of this Time Series can be called like any other pandas series using the index as shown. Sometimes your Time Series may contain duplicated indices. Let us consider the Time Series below. Time Series Analysis comprises of techniques for analyzing Time Series data in an attempt to extract useful statistics and identify characteristics of the data.

Time Series Forecasting is the use of a mathematical model to predict future values based on previously observed values in the Time Series data. Let us now take a look at the graph below, which represents the daily closing price of Aluminium futures over a period of 93 trading days, which is a Time Series. Learning about this very important concept which is Time Series, Mean Reversion is an important inclusion. Well, Mean reversion trading is yet another crucial theory which suggests that prices, returns, or various economic indicators tend to move to the historical average or mean over time.

This theory has led to many trading strategies which involve the purchase or sale of a financial instrument whose recent performance has greatly differed from their historical average without any apparent reason. For example, let the price of gold increase on an average by INR 10 every day and one day the price of gold increases by INR 40 without any significant news or factor behind this rise, then by the mean reversion principle we can expect the price of gold to fall in the coming days such that the average change in price of gold remains the same.

In such a case, the mean revisionist would sell gold, speculating the price to fall in the coming days. Thus, making profits by buying the same amount of gold he had sold earlier, now at a lower price. A mean-reverting Time Series is plotted below to make the understanding easy. Here, the horizontal black line represents the mean and the blue curve is the Time Series which tends to revert back to the mean.

Also, it is important to note here that the collection of random variables is defined to be a stochastic or random process. A stochastic process is said to be Stationary if its mean and variance are constant over time. Furthermore, a Stationary Time Series will be mean reverting in nature, i. Also, a Stationary Time Series will not drift too far away from its mean because of its finite constant variance.

Whereas, a Non-Stationary Time Series, on the contrary, will have a time varying variance or a time varying mean or both, and will not tend to revert back to its mean. In the financial industry, traders take advantage of the Stationary Time Series by placing orders when the price of security deviates considerably from its historical mean, speculating the price to revert back to its mean.

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Let us also observe that, in Non-Stationary, data tends to be unpredictable and cannot be modeled or forecasted. Here, a random walk the movements of an object or changes in a variable that follow no discernible pattern or trend can be transformed into a Stationary series by differencing computing the difference between Yt and Yt One of the simplest mean reversion trading related trading strategies is to find the average price over a specified period, followed by determining a high-low range around the average value from where the price tends to revert back to the mean.

The trading signals will be generated when these ranges are crossed - placing a sell order when the range is crossed on the upper side and a buy order when the range is crossed on the lower side. The trader takes contrarian positions, i.

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This strategy looks too good to be true and it is, it faces severe obstacles. This strategy would result in losses if such a situation arises. Pairs Trading is another strategy that relies on the principle of mean reversion trading. Two co-integrated securities are identified, the spread between the price of these securities would be Stationary and hence mean reverting in nature. An extended version of Pairs Trading is called Statistical Arbitrage, where many co-integrated pairs are identified and split into a buy and sell basket based on the spreads of each pair.

The first step in a Pairs Trading or Stat Arb model is to identify a pair of co-integrated securities. It tests the null hypothesis of a unit root being present in a Time Series sample. A Time Series which has a unit root, i. The augmented Dickey-Fuller statistic, also known as t-statistic, is a negative number. The more negative it is, the stronger the rejection of the null hypothesis that there is a unit root at some level of confidence, which would imply that the Time Series is Stationary.

The t-statistic is compared with a critical value parameter, if the t-statistic is less than the critical value parameter then the test is positive and the null hypothesis is rejected. We start by importing relevant libraries, followed by fetching financial data for two securities using the quandl. Quandl provides financial and economic data directly in Python by importing the Quandl library. We then print the first five rows of the fetched data using the head function, in order to view the data being pulled by the code.

Using the statsmodels. Next, using the statsmodels. This array contains values like the t-statistic, p-value, and critical value parameters. Here, we consider a significance level of 0. So all in all Time Series, its Analysis and Forecasting is quite important and brings us to a great conclusion about it being helpful in predicting Stock prices. We started with understanding the Time Series as to what it means, why is it important to do the analysis as well as forecasting on the basis of it, and how is it done. Coming down to the conclusion, it is an important concept for predicting variables.

The article helped with the core points like:. Disclaimer: All data and information provided in this article are for informational purposes only. All information is provided on an as-is basis. By Chainika Thakar Since predicting the future stock prices in the stock market is crucial for the investors, Time Series and its related concepts hold an exceptional quality of organizing the data for accurate prediction. To support the statement, here are some of the examples of Time Series: Daily stock prices for the last 5 years 1-minute stock price data for the last 90 days Quarterly revenues of a company over the last 10 years Monthly car sales of an automaker for the last 3 years The annual unemployment rate of a state in the last 50 years Coming to Time Series analysis, it simply implies identifying those methods which help in the analysis of Time Series data.

Also, there are few types of Time Series which we will see ahead.

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Autocorrelation function test is mainly used for two reasons: For detecting non-randomness in the data and For identifying the appropriate time series model for the particular dataset. So an Autocorrelation Function Test, therefore, is important for providing accurate results. Python code for ACF- Once you run the python code above, you get a 2D plot of the autocorrelation with first 20 lags: Partial autocorrelation function PACF — In some cases, the effect of autocorrelation at smaller lags will have an influence on the estimate of autocorrelation at longer lags.

Okay now! What are the Components of Time Series Analysis? So, there are three components of a Time Series which are segregated as: Trend - The continuance of increasing or decreasing values in a given Time Series. Seasonal - The repeating cycle over a specific period day, week, month, etc. Structures for the Components or Decomposing As per the nature of the Time Series, it can be presented as an additive or a multiplicative in which each observation is expressed as a sum or a product of the components.

On the basis of the seasonal variation, let us learn the two structures for decomposing the Time Series: Additive decomposition — If the seasonal variation is relatively constant over time, we can use the additive structure for decomposing a given Time Series. What is Time Series Forecasting? Granger Causality Test The Granger Causality test helps you determine if one Time Series will be useful to forecast another one in the future. X- This Time Series led to Y in the past. Note that we are fetching historical data of the "MRF" stock starting 1 st Jan till 31 st Dec The above code gives the output as shown.

The slope of the linear trend b is: The output is as shown: The mean error is: 8. Do let me know in the comments. Ahead, let us see how to import, calculate, and plot Time Series Data in Python. Let us begin with importing this library in our program in python: Importing the required modules from datetime import datetime from datetime import timedelta Hence, the basic tools are discussed here ahead to make the concept clearer.

Converting the output to seconds delta. Conversion Between Strings And DateTime As we mentioned about the conversion of a datetime format, the reverse can also be done and a string which represents a date can be converted to datetime data type.

Output: ' ' datetime. Let us first import pandas. Let us now understand ahead indexing and slicing of a Time Series. So please upload good indicators in this thread. Good indicators are considered: - stimulating your perspective in the market - any trader should know about and should have seen at least once - getting inspiration for your own system - naturally, I don't consider a good indicator: profitable in trading.

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The purpose of this thread is solely increasing our mindset and depth in the market by applying different kind of indicators. Details: complete trade manager, additional windows: correlations, fib level division, multiple methods, TF switcher, currency pair 'wave count' check. Details: choose an indicator, press ok. Recommended 2.

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