Index (RSI) AlgorithmBuilding on the momentum from the Moving Average algorithm, you’ll now try to develop a more sophisticated algorithm to outdo it! With this algorithm you will make trading decisions with Relative Strength Index, or RSI.How does this algorithm compare to your moving average algorithm?Algorithm DescriptionThe relative strength index (RSI) is defined as a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock. The index is calculated by the average gain and average loss over a rolling time window.???????????=?????????/????????AverageGain=TotalGain/Lookback?????Gains: When there was a price increase compared to the previous day, the gain is equal to that price gain value, otherwise set to zero.?????????TotalGain: The sum of the gains over the lookback period.????????Lookback: The number of days of previous data to look back.???????????=?????????/????????AverageLoss=TotalLoss/Lookback??????Losses: When there was a price decrease compared to the previous day, the loss is equal to that price loss value, otherwise set to zero.?????????TotalLoss: The sum of the losses over the lookback period.????????Lookback: The number of days of previous data to look back. The typically used value for the “lookback” is 14 or 21 days.???=100−(100/(1+(???????????/???????????)))RSI=100−(100/(1+(AverageGain/AverageLoss)))Your task is to develop a Python program to calculate the RSI and see if the market is trending towards a positive or a negative momentum associated with a stock. In your code, a trading decision needs to be made based on RSI.Buying position: When the RSI for the stock is below 30, it indicates a good buying position. This signifies that the market is oversold.Selling position: When the RSI for the stock is above 70, it indicates a good selling position. This signifies that the market is overbought.On each day, you need to make trading decisions on two distinct data files based on the criteria above. You can test using AAPL.csv and MSFT.csv, but I may test with any two stock data files from random companies.You can trust that any tested stock files will have the same date ranges for the data.A starting balance of $10,000 is recommended.Additional Requirements¶For each company, you must buy, or sell 10 stocks, or less per transaction.You are free to choose which column of stock data to use (open, close, low, high, etc).When your algorithm reaches the last day of data, have it sell all remaining stock. Your function will return the number of stocks you own (should be zero, at this point), and your cash balance.You are not allowed to “look into the future” in your data. Your simulation has the data for the current day, and the past.You are not allowed to use test_data() directly in Milestone III, so be sure to write new functions that use what you learned from writing that function.You must use the transact() function you wrote in Milestone I, as it will help you make some of the basic decisions for buying and selling you will undoubtedly make.def alg_rsi(filename_1, filename_2): """This function implements the Relative Strength Index algorithm. Using the CSV stock data from two stock files that are loaded into your program, use that data to make decisions using the Relative Strength Index (RSI) algorithm. Algorithm: [REPLACE THIS WITH A CLEAR DESCRIPTION OF THE ALGORITHM.] [I AM LEAVING THIS YOUR RESPONSIBILITY TO FILL OUT.] Arguments: filename_1 (str): A filename, as a string, for one set of stock data for a first company. filename_2 (str): A filename, as a string, for one set of stock data for a second company. Returns: Two values, stocks and balance OF THE APPROPRIATE DATA TYPE. Prints: Nothing. """ # Last thing to do, return two values: one for the number of stocks you # end up owning after the simulation, and the amount of money you have # after the simulation. Remember, all your stocks should be sold at the # end! return stocks_owned, cash_balance AAPLDateOpenHighLowCloseAdj CloseVolume2000-01-033.7455364.0178573.6316963.9977682.6959201339492002000-01-043.8660713.9508933.6138393.6607142.4686261280944002000-01-053.7053573.9486613.6785713.7142862.5047511945804002000-01-063.7901793.8214293.3928573.3928572.2879941919932002000-01-073.4464293.6071433.4107143.5535712.3963731151836002000-01-103.6428573.6517863.3839293.4910712.3542261262660002000-01-113.4263393.5491073.2321433.3125002.233805110387200 DateOpenHighLowCloseAdj CloseVolume2000-01-0358.68750059.31250056.00000058.28125042.641369532284002000-01-0456.78125058.56250056.12500056.31250041.200928541190002000-01-0555.56250058.18750054.68750056.90625041.635361640596002000-01-0656.09375056.93750054.18750055.00000040.240646549766002000-01-0754.31250056.12500053.65625055.71875040.76651062013600

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Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Index (RSI) Algorithm

Building on the momentum from the Moving Average algorithm, you’ll now try to develop a more sophisticated algorithm to outdo it! With this algorithm you will make trading decisions with Relative Strength Index, or RSI.

How does this algorithm compare to your moving average algorithm?

Algorithm Description

The relative strength index (RSI) is defined as a momentum indicator that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock. The index is calculated by the average gain and average loss over a rolling time window.

???????????=?????????/????????AverageGain=TotalGain/Lookback
- ?????Gains: When there was a price increase compared to the previous day, the gain is equal to that price gain value, otherwise set to zero.
- ?????????TotalGain: The sum of the gains over the lookback period.
- ????????Lookback: The number of days of previous data to look back.
???????????=?????????/????????AverageLoss=TotalLoss/Lookback
- ??????Losses: When there was a price decrease compared to the previous day, the loss is equal to that price loss value, otherwise set to zero.
- ?????????TotalLoss: The sum of the losses over the lookback period.
- ????????Lookback: The number of days of previous data to look back. The typically used value for the “lookback” is 14 or 21 days.
???=100−(100/(1+(???????????/???????????)))RSI=100−(100/(1+(AverageGain/AverageLoss)))

Your task is to develop a Python program to calculate the RSI and see if the market is trending towards a positive or a negative momentum associated with a stock. In your code, a trading decision needs to be made based on RSI.

Buying position: When the RSI for the stock is below 30, it indicates a good buying position. This signifies that the market is oversold.
Selling position: When the RSI for the stock is above 70, it indicates a good selling position. This signifies that the market is overbought.

On each day, you need to make trading decisions on two distinct data files based on the criteria above. You can test using AAPL.csv and MSFT.csv, but I may test with any two stock data files from random companies.

You can trust that any tested stock files will have the same date ranges for the data.

A starting balance of $10,000 is recommended.

Additional Requirements¶

For each company, you must buy, or sell 10 stocks, or less per transaction.
You are free to choose which column of stock data to use (open, close, low, high, etc).
When your algorithm reaches the last day of data, have it sell all remaining stock. Your function will return the number of stocks you own (should be zero, at this point), and your cash balance.
You are not allowed to “look into the future” in your data. Your simulation has the data for the current day, and the past.
You are not allowed to use test_data() directly in Milestone III, so be sure to write new functions that use what you learned from writing that function.
You must use the transact() function you wrote in Milestone I, as it will help you make some of the basic decisions for buying and selling you will undoubtedly make.

def alg_rsi(filename_1, filename_2): """This function implements the Relative Strength Index algorithm. Using the CSV stock data from two stock files that are loaded into your program, use that data to make decisions using the Relative Strength Index (RSI) algorithm. Algorithm: [REPLACE THIS WITH A CLEAR DESCRIPTION OF THE ALGORITHM.] [I AM LEAVING THIS YOUR RESPONSIBILITY TO FILL OUT.] Arguments: filename_1 (str): A filename, as a string, for one set of stock data for a first company. filename_2 (str): A filename, as a string, for one set of stock data for a second company. Returns: Two values, stocks and balance OF THE APPROPRIATE DATA TYPE. Prints: Nothing. """ # Last thing to do, return two values: one for the number of stocks you # end up owning after the simulation, and the amount of money you have # after the simulation. Remember, all your stocks should be sold at the # end! return stocks_owned, cash_balance

AAPL

Date	Open	High	Low	Close	Adj Close	Volume
2000-01-03	3.745536	4.017857	3.631696	3.997768	2.695920	133949200
2000-01-04	3.866071	3.950893	3.613839	3.660714	2.468626	128094400
2000-01-05	3.705357	3.948661	3.678571	3.714286	2.504751	194580400
2000-01-06	3.790179	3.821429	3.392857	3.392857	2.287994	191993200
2000-01-07	3.446429	3.607143	3.410714	3.553571	2.396373	115183600
2000-01-10	3.642857	3.651786	3.383929	3.491071	2.354226	126266000
2000-01-11	3.426339	3.549107	3.232143	3.312500	2.233805	110387200

Date	Open	High	Low	Close	Adj Close	Volume
2000-01-03	58.687500	59.312500	56.000000	58.281250	42.641369	53228400
2000-01-04	56.781250	58.562500	56.125000	56.312500	41.200928	54119000
2000-01-05	55.562500	58.187500	54.687500	56.906250	41.635361	64059600
2000-01-06	56.093750	56.937500	54.187500	55.000000	40.240646	54976600
2000-01-07	54.312500	56.125000	53.656250	55.718750	40.766510	62013600

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