Portfolio risk management is a crucial aspect of investment strategy that aims to mitigate potential losses and optimize returns. In this project I used Algorithmic Trading to better understand the stocks or internal working of the following companies like Meta, Netflix, Google, Amazon and Microsoft to understand the cause behind reduce hiring, impose a hiring freeze and suspend pay raises, resorting to layoffs to cut costs if all else fails after Covid-19.

Using computers to make investment decisions.

Suppose a trader follows these simple trade criteria: -- Buy 50 shares of a stock when its 50-day moving average goes above the 200-day moving average. (A moving average is an average of past data points that smooths out day-to-day price fluctuations and thereby identifies trends.)
-- Sell shares of the stock when its 50-day moving average goes below the 200-day moving average.

Using these two simple instructions, a computer program will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity.

Python for Model Building

The process of ruinning a quantitative investing strategy can be broken down into the following steps:

1. Collect data
2. Develop a hypothesis for a strategy
3. Backtest that Strategy
4. Implement the strategy in production

The interplay between company profits, share prices, and economic conditions is intricate. Stagnant profits can make companies vulnerable during a recession, prompting various cost-cutting measures to navigate financial challenges.