![]() Ratio Call Spread: sell 1 call on the at-the-money strike and buy 2 calls on the 25-delta strike for that expiration. Strangle: buying or selling 1 call on the 25-delta (out-of-the-money) strike and 1 put on the 25-delta (out-of-the-money) strike for that expiration. ATM = At-the-Money (nearest strike to the spot price)ĪTM Straddle: buying or selling 1 call and 1 put on the same strike for the strike nearest to the at-the-money price for that expiration.ĪTM Call: buying or selling 1 call on the strike nearest to the at-the-money price for that expiration.ĪTM Put: buying or selling 1 put on the strike nearest to the at-the-money price for that expiration.Ģ5-Delta Call: buying or selling 1 call on the strike nearest to the 25-delta (out-of-the-money) for that expiration.Ģ5-Delta Put: buying or selling 1 put on the strike nearest to the 25-delta (out-of-the-money) for that expiration.ħ5-Delta Call: buying or selling 1 call on the strike nearest to the 75-delta (in-the-money) for that expiration.ħ5-Delta Put: buying or selling 1 put on the strike nearest to the 75-delta (in-the-money) for that expiration.īull Call Spread: buy 1 call on the at-the-money strike and sell 1 call on the 25-delta strike for that expiration.īear Put Spread: buy 1 put on the at-the-money strike and sell 1 put on the 25-delta strike for that expiration. All strategies are assumed to be Long (buying) unless otherwise noted. The type of the selected earnings option strategy. dynamoTable.Current Strategy Market & Theoretical Value #Appending the data into the DynamoDB table. #Storing data into local variables time_str = str( df. #cli.py contains a function named start1 which scrapes the web and returns a single row dataframe with the stock prices at that time. resource( 'dynamodb')ĭynamoTable = dynamodb. request import urlopen from bs4 import BeautifulSoup import boto3 import time import csv import test import prediction import scrape import analysis dynamodb = boto3. Import json import cli import pandas as pd import numpy as np from urllib. This lambda function fetches the data from DynamoDB and stores it in a S3 Bucket as a JSON format.Īs both lambda functions are interconnected, new data will be updated every minute and stored into the DynamoDB as well as the S3 bucket. The second lambda function (AWS Lambda Function 2) is triggered by a DynamoDB event and is executed when there is an addition/reduction of data in/from the DynamoDB table. ![]() The lambda function scapes for the stock price of Amazon, NASDAQ, S&P 50, & DowJones, formats the data into a Pandas DataFrame, and stores this data in a DynamoDB table. The first lambda function (AWS Lambda Function 1) is triggered by a CloudWatch event which executes the function every minute from 9:30 am to 4 pm Monday - Friday (When the stock market is open). This phase consists of using Amazon DynamoDB, Amazon S3, Amazon CloudWatch and two Amazon Lambda functions. This project is broken down into 2 phases. I have provided my Dockerhub Credentials to the repo using Github Secrets. Once it successfully passses the Make Install and Make Lint phase, CD takes place where it automatically builds the new Docker Container (Image) and pushes it to Dockerhub. CI is done by Make Install and Make Lint which automatically ensures the updated code has no errors. This project uses CI and CD through GitHub Actions. Detailed Walkthrough of the Project, Code, and Live App Demo - Link to DemoĬontinuous Integration and Continuous Deployment (CI/CD).Brief Overview of the Project - Link to Demo.The "Predict" button will then run the prediction algorithm (SARIMAX Time Series Forecasting) and display the predictions on the Application. The "Refresh" button will automatically restart the application and extract the most current stock price from the S3 bucket. This project is useful for those actively selling and buying stocks in a day.Īs shown in the Figure above, the application consists of 2 buttons. The main objective of this project is to predict the next minute of Amazon Stock Price based on the current stock price and the previous 150 observations. Welcome to the Real Time Stock Prediction Application. Noah Gift for his constant support and help. Real Time Amazon Stock Prediction Dash Application - AWS - IDS 706 Final Project Overview of ReadME
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |