def smoothMAcum(depth,temp, scale): # Moving average by cumsum, scale = window size in m dz = np.diff(depth) N = int(scale/dz[0]) cumsum = np.cumsum(np.insert(temp, 0, 0)) smoothed=(cumsum[N:] - cumsum[:-N]) / N return smoothed def smoothMAconv(depth,temp, scale): # Moving average by numpy convolution dz = np.diff(depth) N = int(scale/dz[0]) smoothed=np.convolve(temp, np.ones((N,))/N, mode='valid') return smoothe Actually I found another implementation in python docs. def moving_average(iterable, n=3): # moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0 # http://en.wikipedia.org/wiki/Moving_average it = iter(iterable) d = deque(itertools.islice(it, n-1)) d.appendleft(0) s = sum(d) for elem in it: s += elem - d.popleft() d.append(elem) yield s / Let's also quickly calculate the simple moving average for a window_size of 4. for i in range(0,df.shape[0]-3): df.loc[df.index[i+3],'SMA_4'] = np.round(((df.iloc[i,1]+ df.iloc[i+1,1] +df.iloc[i+2,1]+df.iloc[i+3,1])/4),1) df.head(

The most commonly used Moving A verages (MAs) are the simple and exponential moving average. Simple Moving Average (SMA) takes the average over some set number of time periods. So a 10 period SMA would be over 10 periods (usually meaning 10 trading days). The Simple Moving Average formula is a very basic arithmetic mean over the number of periods A moving average requires that you specify a window size called the window width. This defines the number of raw observations used to calculate the moving average value. The moving part in the moving average refers to the fact that the window defined by the window width is slid along the time series to calculate the average values in the new series Understand Moving Average Filter with Python & Matlab. The moving average filter is a simple Low Pass FIR (Finite Impulse Response) filter commonly used for smoothing an array of sampled data/signal. It takes samples of input at a time and takes the average of those -samples and produces a single output point So a 10 moving average would be the current value, plus the previous 9 months of data, averaged, and there we would have a 10 moving average of our monthly data. Doing this is Pandas is incredibly fast. Pandas comes with a few pre-made rolling statistical functions, but also has one called a rolling_apply ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of model that captures a suite of different standard temporal structures in time series data. In this tutorial, you will discover how to develop an ARIMA model for time series forecasting in Python. After completing this tutorial, you will know

I would like to calculate the average of the neighbors of the upper left cell (value 13). The desired calculation will be. (21+10+5)/3 = 12. For the pixel in the first column and second row (value 5) the desired calculation will be -. (13+21+10+5)/4 = 12.25 standard_deviation(samples_in_window) / sqrt(size(samples_in_window)) On the predictive value side, this is the difference between the mean of all samples and the mean of samples within the window. So, our task is to select the window size that maximizes predictive accuracy, which is the predictive value minus the predictive error

Using a single Moving Average - A single moving average can be used to generate trade signals. When the closing price moves above the moving average, a buy signal is generated and vice versa. When using a single moving average one should select the averaging period in such a way that it is sensitive in generating trading signals and at the same time insensitive in giving out false signals If the extent or the period, m is odd i.e., m is of the form (2k + 1), the moving average is placed against the mid-value of the time interval it covers, i.e., t = k + 1. On the other hand, if m is even i.e., m = 2k, it is placed between the two middle values of the time interval it covers, i.e., t = k and t = k + 1

Moving average is nothing but the average of a rolling window of defined width. But you must choose the window-width wisely, because, large window-size will over-smooth the series. For example, a window-size equal to the seasonal duration (ex: 12 for a month-wise series), will effectively nullify the seasonal effect Let's say you only need to know where the moving average is as of right now. Or as of the last price point in the DataFrame. We can use the same mean() function and just run it on the last 20 rows of the DataFrame like this: # calculate just the last value for the 20 moving average mean = btc_df.close.tail(20).mean( Let \(w_t \overset{iid}{\sim} N(0, \sigma^2_w)\), meaning that the w t are identically, independently distributed, each with a normal distribution having mean 0 and the same variance. The 1 st order moving average model, denoted by MA(1) is: \(x_t = \mu + w_t +\theta_1w_{t-1}\) The 2 nd order moving average model, denoted by MA(2) is: \(x_t. * In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set*. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include: simple, cumulative, or weighted forms (described below)

We can also plot moving averages with the mav keyword. use a scalar for a single moving average; use a tuple or list of integers for multiple moving averages; mpf. plot (daily, type = 'ohlc', mav = 4) mpf. plot (daily, type = 'candle', mav = (3, 6, 9) # Define parameters for the walk dims = 2 step_n = 10000 step_set = [-1, 0, 1] origin = np.zeros((1,dims)) # Simulate steps in 2D step_shape = (step_n,dims) steps = np.random.choice(a=step_set, size=step_shape) path = np.concatenate([origin, steps]).cumsum(0) start = path[:1] stop = path[-1:] # Plot the path fig = plt.figure(figsize=(8,8),dpi=200) ax = fig.add_subplot(111) ax.scatter(path[:,0], path[:,1],c='blue',alpha=0.25,s=0.05); ax.plot(path[:,0], path[:,1],c='blue',alpha=0.5,lw. Compute the three-point centered moving average of a row vector. When there are fewer than three elements in the window at the endpoints, take the average over the elements that are available. A = [4 8 6 -1 -2 -3 -1 3 4 5]; M = movmean(A,3 ** In Python, the statistics**.median() function is used to calculate the median value of a data set. statistics.median() is part of the statistics Python module. It includes a number of functions for statistical analysis. First, import the statistics module with this code Varun May 6, 2019 How to get Numpy Array Dimensions using numpy.ndarray.shape & numpy.ndarray.size() in Python 2019-05-06T07:55:11+05:30 Numpy, Python No Comment In this article we will discuss how to count number of elements in a 1D, 2D & 3D Numpy array, also how to count number of rows & columns of a 2D numpy array and number of elements per axis in 3D numpy array

Sum and average of n numbers in Python. Accept the number n from a user. Use input() function to accept integer number from a user.. Run a loop till the entered number. Next, run a for loop till the entered number using the range() function. In each iteration, we will get the next number till the loop reaches the last number, i.e., n. Calculate the su * Every function takes the same input, passed as a dictionary of Numpy arrays: import numpy as np # note that all ndarrays must be the same length! inputs = { 'open' : np *. random . random ( 100 ), 'high' : np . random . random ( 100 ), 'low' : np . random . random ( 100 ), 'close' : np . random . random ( 100 ), 'volume' : np . random . random ( 100 ) By default, `virtual_batch_size` is `None`, which means batch normalization is performed across the whole batch. When. `virtual_batch_size` is not `None`, instead perform Ghost Batch. Normalization, which creates virtual sub-batches which are each. normalized separately (with shared gamma, beta, and moving statistics)

Python: cv.MIXED_CLONE. The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective. MONOCHROME_TRANSFER Thereafter, we have set the labels and title to our Python Subplot. Finally, we have formatted the date to our liking (read more from here) and used plt.show() to plot the Python Candlestick Chart. Matplotlib Candlestick chart with SMA Overlay in Python. We can also overlay the Simple Moving Average(SMA) on the Matplotlib Candlestick chart The formula for the weighted **moving** **average** is expressed as follows: Where: N is the time period. 4. Add up resulting values to get the weighted **average**. The final step is to add up the resulting values to get the weighted **average** for the closing prices of ABC Stock. WMA = $30 + $23.47 + $17.80 + $12 + $6.07

Exponential Moving Average. You might have seen some articles on the internet using very complex models and predicting almost the exact behavior of the stock market. But beware! These are just optical illusions and not due to learning something useful. You will see below how you can replicate that behavior with a simple averaging method. In the. Description. The Burmese python is a dark-colored non-venomous snake with many brown blotches bordered in black down the back. In the wild, Burmese pythons typically grow to 5 m (16 ft), while specimens of more than 7 m (23 ft) are uncommon. This species is sexually dimorphic in size; females average only slightly longer, but are considerably heavier and bulkier than the males * Then it calculates a second simple moving average on the first moving average with the same window size*. output = tsmovavg( vector , 't' , numperiod , dim ) returns the triangular moving average for a vector IMovingAverage avg = new WeightedMovingAverage(10); and call the exact same Test routine to see how the new moving average works.By using the interface, we promote the usability of the code. All This Work! Admittedly, this is a lot of work for a simple algorithm i have 3 data sets of 501 frames each and want to get a moving average of all 3 graphs in a single graph. i am able to lot all 3 graphs in one but unable to get a moving graph through xmgrace. any.

- Machine Learning is widely used for classification and forecasting problems on time series problems. When there is a predictive model to predict an unknown variable; where time acts as an independent variable and a target-dependent variable, time-series forecasting comes into the picture.. Become A Chartered Data Scientist™ Achieve the highest distinction in the data science professio
- Based on the feedback i got on my original VI i have refined the Moving Average code into a subVI. I then used it to average a simulated 10Channel data - just to keep things simple i made sure all10 Channels had identical data. One would then expect to get the same moving average for all 10 channels
- The moving average method is one of the most fundamental concept not only in time series analysis but also in machine learning. It acts as a baseline model for the time series data.. Moving average smoothing is applicable for estimating the trend-cycle of the past values
- Let's consider the same dataset that we have taken in average. First, calculate the deviations of each data point from the mean, and square the result of each, Variance in Python Using Numpy: One can calculate the variance by using numpy.var() function in python. Syntax: numpy.var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>
- Is it possible to implement a moving average in C without the need for a window of samples? I've found that I can optimize a bit, by choosing a window size that's a power of two to allow for bit-shifting instead of dividing, but not needing a buffer would be nice
- Moving Average Filters The moving average is the most common filter in DSP, mainly because it is the easiest digital filter to understand and use. In spite of its simplicity, the moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response. This makes it th

$\begingroup$ y is the moving average calculated from the original signal x by multiplying by A. This procedure give us a signal z which has the same moving average y. Therefore y=A.z. So only the norm of z gets minimized. If the original signal happen to have a large norm value, then the procedure will not give good results Arnaud Legoux moving average or ALMA for short is a recent addition to the family of moving average technical indicators. Developed by Arnaud Legoux and Dimitrios Kouzis Loukas, the ALMA was created as recently as 2009. Despite being new, the ALMA has quickly caught on to the trading community * Line 4-5: Call the function to calculate the moving average by giving the stock data and window size as parameters*. The returned moving average values are stored in the variable df_ma. The next step is to use the moving average values to generate a multi-line plot using the Plotly graphing library

- One of the more popular rolling statistics is the moving average. This takes a moving window of time, and calculates the average or the mean of that time period as the current value. In our case, we have monthly data. So a 10 moving average would be the current value, plus the previous 9 months of data, averaged, and there we would have a 10.
- import pandas as pd import matplotlib.pyplot as plt import numpy as np import math dataset = pd.read_csv(data.csv) #Calculate moving average with 0.75s in both directions, then append do dataset hrw = 0.75 #One-sided window size, as proportion of the sampling frequency fs = 100 #The example dataset was recorded at 100Hz mov_avg = dataset['hart'].rolling(int(hrw*fs)).mean() #Calculate moving.
- Triangular Moving Average¶ Another method for smoothing is a moving average. There are various forms of this, but the idea is to take a window of points in your dataset, compute an average of the points, then shift the window over by one point and repeat. This will generate a bunch of points which will result in the smoothed data

Establishing a baseline is essential on any time series forecasting problem. A baseline in performance gives you an idea of how well all other models will actually perform on your problem. In this tutorial, you will discover how to develop a persistence forecast that you can use to calculate a baseline level of performance on a time series dataset with Python Model Average Checkpoint. callbacks.ModelCheckpoint doesn't give you the option to save moving average weights in the middle of training, which is why Model Average Optimizers required a custom callback. Using the update_weights parameter, ModelAverageCheckpoint allows you to: Assign the moving average weights to the model, and save them

Is there maybe a better approach to calculate the exponential weighted moving average directly in NumPy and get the exact same result as the pandas.ewm().mean()? At 60,000 requests on pandas solution, I get about 230 seconds ** Moving averages are built by first specifying a histogram or date_histogram over a field**. You can then optionally add normal metrics, such as a sum, inside of that histogram.Finally, the moving_avg is embedded inside the histogram. The buckets_path parameter is then used to point at one of the sibling metrics inside of the histogram (see buckets_path Syntax for a description of the syntax. Calculating a Moving Average. Since a stock price can be volatile, it can be helpful to look at its moving average, which appears smoother and gives us an idea of the overall trend. I decided to find the average price over the past 100 days. Here's the code, and a plot that shows 'Close' and its moving average BBANDS Bollinger Bands DEMA Double Exponential Moving Average EMA Exponential Moving Average HT_TRENDLINE Hilbert Transform - Instantaneous Trendline KAMA Kaufman Adaptive Moving Average MA Moving average MAMA MESA Adaptive Moving Average MAVP Moving average with variable period MIDPOINT MidPoint over period MIDPRICE Midpoint Price over period SAR Parabolic SAR SAREXT Parabolic SAR - Extended. The following are 12 code examples for showing how to use bokeh.models.DatetimeTickFormatter().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example

- In our Python notebook, we are going to create a new column mvg_avg in our Dataframe that represents the equivalent of the 14-day moving average we previously calculated using SQL. To do this using pandas, we first select the column we want to apply our window function on ( trips ) from our Dataframe as a Series object by using df.trips
- A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, The structure is the same as the implementation of Standard Deviation#E. pragma. enable def simple_moving_average = {size-> def nums = [] double total = 0.0 return {newElement ->
- I thought translating some of his work to Python could help others who are less familiar with R. I have also adapted code from other bloggers as well. See References. This article is a living document. I will update it with corrections as needed and more useful information as time passes. Before we begin let's import our Python libraries
- Same as in my previous tutorial, first before moving ahead, let's implement this for a random-action AI agent interacting with this environment. Create a new python file named BipedalWalker-v2_random.py by copying and executing the following code
- Calculating Moving Average. Example: Let's consider having the following 5 prices, one per day, that we want to calculate moving average. Day1: 5000 Day2: 5390 Day3: 5500 Day4: 5400 Day5: 5700. If we are calculating moving average with size of 5, we would calculate the average from all 5 days from above: Sum = 5000 + 5390 + 5500 + 5400 + 5700.
- Based on a 4-day exponential moving average the stock price is expected to be $31.50 on the 13 th day. Explanation. The formula for simple moving average can be derived by using the following steps: Step 1: Firstly, decide on the number of the period for the moving average, such as 2-day moving average, 5-day moving average, etc

- Two filters are currently supported: a simple moving average and an FIR filter. Code is written in ARM Thumb assembler for performance. They operate on 32 bit signed integers. On the MicroPython board the moving average takes 8uS and the FIR takes 15uS for a typical set of coefficients
- Python Average via Loop. In this example, the code size is reduced. The average can be calculated with just one line of code as shown below. Program Example: # Example to find average of list number_list = [45, 34, 10, 36, 12, 6, 80] avg = sum.
- $\begingroup$ I am not into python but looks like that your average (ave) Make sure the rolling window is the same for both the upper & lower bands $\endgroup$ - Rime Dec 11 '14 at 6:45. Add a comment | Calculating a Linear Weighted Moving Average in Python. 1

M is the same size as A. If A is a vector, then movmean operates along the length of the vector. Compute the three-point centered moving average for each row of a matrix. The window starts on the first row, slides horizontally to the end of the row, then moves to the second row, and so on Possibly the simplest form of forecasting is the moving average.Often, a moving average is used as a smoothing technique to find a straighter line through data with a lot of variation. Each data point is adjusted to the value of the average of n surrounding data points, with n being referred to as the window size. With a window size of 10, for example, we would adjust a data point to be the. First, we need to define functions plot_series() and moving_average_predict(). The plot_series() function will be used several times for creation of plots with time series. The moving_average_predict() function takes time series and window size as inputs and generates the predictions for the whole time series Moving average means that any given value v(t) in the series can be explained by a function of its one or more previous errors only, like e(t-1). In some cases, the series could be related to two or more past errors as well. Statsmodels is part of th

Python HTML Reports in Python/v3 How to make HTML reports with Python, Pandas, and Plotly Graphs. Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version ** A weighted moving average is a moving average where within the sliding window values are given different weights, typically so that more recent points matter more**. Ins tead of selecting a window size, it requires a list of weights (which should add up to 1)

Zipline is a Python library for trading applications. It is an event-driven system that supports both backtesting and live trading. In this article, we will learn how to install Zipline and then how to implement Moving Average Crossover strategy and calculate P&L, Portfolio value etc. This article is divided into the following four sections Average of the list = 35.75 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics. To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Cours

Python Pandas - Window Functions expanding and exponentially moving weights for window statistics. Note − Since the window size is 3, for first two elements there are nulls and from third the value will be the average of the n, n-1 and n-2 elements. Thus we can also apply various functions as mentioned above Backtesting the dual-**moving** **average** trading strategy. The dual-**moving** **average** trading strategy places a buy order when the short **moving** **average** crosses the long **moving** **average** in an upward direction and will place a sell order when the cross happens on the other side

- As you see, we use the nice way to generate the random data by calling the random.uniform, which takes the lower bound and upper bound of the uniform interval to get numbers from, and the size parameter is the dimension of the NumPy array. Here it generates 100 rows in 4 columns. The DataFrame constructor can convert the NumPy array to the DataFrame and setting the column names to A, B, C, and.
- In this tutorial, we will learn how to calculate the curvature of a curve in Python using numpy module. We will calculate other quantities also like velocities, acceleration, and more. You can find the necessary formulas for them in the picture below
- When you launch it, you will find the model automatically launches in virtual time mode. In the baseline scenario, the queue capacity is 3, the resource load will be 0.83, and the average service queue size will be 2.54 Now we will run the same model using the AnyLogic Cloud API and Python but with a different parameter value for Server Capacity
- Generating Trade Signals using Moving Average(MA) Crossover Strategy — A Python implementation Published on October 10, 2020 October 10, 2020 • 6 Likes • 0 Comment
- The weights have all the same value = 1 / (filter size left + filter size right + 1). This filter is also called moving average, moving mean, rolling average, rolling mean or running average. binom: Symmetric filter with filter size (=q) values each left and right to the actual value
- Ball Pythons are one of the smallest breeds of python in Africa. These snakes are slow moving and have a stocky body shape. They are also one of the calmest species, making them very popular. Unlike many snakes, there is a large size difference between males and females. Males and females are hatched the same size and weight

- Kaufman's Adaptive Moving Average (KAMA) Moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance
- @om_henners gives a generic_filter method that works well for small arrays, which is the intended use case from the original question; however, this method can be slow for medium and large arrays. A similar approach using convolve2d will produce identical results and can provide substantial speed improvements, as demonstrated below. With a (2048, 512) array, I see a speedup of ~300 when using.
- 9. I have read in many places that Moving median is a bit better than Moving average for some applications, because it is less sensitive to outliers. I wanted to test this assertion on real data, but I am unable to see this effect (green: median, red: average). See here: Here's the sample audio data test.wav
- Moving average filters are filters calculating a series of How to decide the moving window size (i.e. 3*3, 5*5 My question is what I should do with the timestamps at the same time
- A moving average is, by definition, the average of some number of previous data points. In the case of continuous function f: R → R, we can define the simple moving average (SMA) with window size R ∋ w > 0 to be the function. f ¯ w ( x) = 1 w ∫ x − w x f ( y) d y. In the case of a discrete function g: Z → R as likely in the case of.

S&P 100 portfolio test results: As you can see from the table, the best moving average for a 5/20 day crossover was the exponential moving average (EMA) which gave a compounded annualised return of 3.6% and a maximum drawdown of -34%, resulting in a CAR/MDD of 0.11. The worst performing moving average was the least squares ema = tf.train.ExponentialMovingAverage(decay=0.9999) with tf.control_dependencies( [opt_op]): # Create the shadow variables, and add ops to maintain moving averages. # of var0 and var1. This also creates an op that will update the moving. # averages after each training step

3 which a moving average might be computed, but the most obvious is to take a simple average of the most recent m values, for some integer m. This is the so-called simple moving average model (SMA), and its equation for predicting the value of Y at time t+1 based on data up to time t is and same direction modes. The Python III is simply the best basic radar package available, with great looks, The Python Series III Moving Radar is designed for convenient use by law enforcement agencies in One mile range typical for an average size vehicle. Range varies with vehicle size, terrain, weather, an

So, the moving average for January 9, 2020 is the average of these three values, or 1,306.66 as shown in the image above. The moving average is calculated in the same way for each of the remaining dates, totaling the three stock prices from the date in question and the two previous days then dividing that total by 3 This post focuses on a particular type of forecasting method called ARIMA modeling. ARIMA, short for 'AutoRegressive Integrated Moving Average', is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values. 2

1.3 CandleStick Layout, Styling and Moving Average Lines ¶. We can try various styling functionalities available with mplfinance.We can pass the color of up, down and volume bar charts as well as the color of edges using the make_marketcolors() method. We need to pass colors binding created with make_marketcolors() to make_mpf_style() method and output of make_mpf_style() to style attribute. Hello everyone, In this tutorial, we'll be discussing Time Series Analysis in Python which enables us to forecast the future of data using the past data that is collected at regular intervals of time. Then we'll see Time Series Components, Stationarity, ARIMA Model and will do Hands-on Practice on a dataset. Let us start this tutorial with the definition of Time Series Backtrader for Backtesting (Python) - A Complete Guide. If you want to backtest a trading strategy using Python, you can 1) run your backtests with pre-existing libraries, 2) build your own backtester, or 3) use a cloud trading platform. Option 1 is our choice. It gets the job done fast and everything is safely stored on your local computer The EWMA chart will detect shifts of .5 sigma to 2 sigma much faster than Shewhart charts with the same sample size. They are, however, slower in detecting large shifts in the process mean. Another advantage is that each data point plotted on the chart is represents a moving average of points

Python Exercise #1B - Field Size Calculator. The field size of a radiation beam is generally defined using the 50% isodose line. When examining cross-plane or in-plane dose profiles, this can be referred to as the full width at half maximum (FWHM). The second python exercise, detailed here, was to develop software for the determination of. Solution: Here, the 4-yearly moving averages are centered so as to make the moving average coincide with the original time period. It is done by dividing the 2-period moving totals by two i.e., by taking their average. The graphic representation of the moving averages for the above data set is In Python, the statistics.median () function is used to calculate the median value of a data set. statistics.median () is part of the statistics Python module. It includes a number of functions for statistical analysis. First, import the statistics module with this code: Now we can use the median () function

Since all the moving-average indicators are declared at the initialization of the strategy, each one warms-up at the same time. This was not the case before; lots of data was lost to the warm-up period for every training/testing set Value Vector the same length as time series x. Details Types of available moving averages are: s for ``simple'', it computes the simple moving average.n indicates the number of previous data points used with the current data point when calculating the moving average.; t for ``triangular'', it computes the triangular moving average by calculating the first simple moving average with window. Guppy multiple moving average (GMMA) is one of the most popular and accurate methods to identify the ongoing trend as well as trend reversals. It was developed by Daryl Guppy, an Australian trader who has put years of effort in developing this amazing indicator. In this post, we would explore a trading system based on Guppy multiple moving average

A moving average filter requires no multiplies, only two additions, two incrementing pointers, and some block RAM. Although the filter has a -13 dB stopband, applying the filter in a cascaded fashion N times would give you a -13 * N dB stopband . Six rounds of such a filter may well be sufficient, especially when each moving average round uses. Write a Python function rainaverage(l) that takes as input a list of rainfall recordings and computes the avarage rainfall for each city.The output should be a list of pairs (c,ar) where c is the city and ar is the average rainfall for this city among the recordings in the input list.Note that ar should be of type float.The output should be sorted in dictionary order with respect to the city name Make sure the Image and rectangle are of the **same** **size**, else there will be issues during collision detection. changed several lines and even removed some lines. Take a good look at the code before **moving** on to the explanation. but considering the **average** game created in **Python** pygame, it's still very small Hello Algotrading! A classic Simple Moving Average Crossover strategy, can be easily implemented and in different ways. The results and the chart are the same for the three snippets presented below. from datetime import datetime import backtrader as bt # Create a subclass of Strategy to define the indicators and logic class SmaCross ( bt