Algebra 2 Student 1 Initial Values: Current Position = 0 ✓ Solved

Algebra 2 Student 1 INITIAL VALUES: current_position = 0

INITIAL VALUES: current_position = 0, low_price = 0, high_price = 0

STEP 1: Set the low_price to the stock price at the current_position. Set the high_price to the stock price at the current_position.

STEP 2: Set the current_position to the subsequent position.

STEP 3: Compare the low_price with the stock price at the current_position.

STEP 4: If the low_price is less than the stock at the current_position then do the following:

Compare the high_price with the stock at the current_position. If the high_price is less than the stock at the current_position, set the high_price to the stock at the current_position. Go to STEP 2. If the low_price is greater than the stock at the current_position then do the following: Set the low_price as the stock at the current_position. Set the high_price to the stock at the current_position.

EXAMPLE: Here is an example of the values that are set through an iteration of the algorithm.

Input = [143, 122, 121, 119, 147, 170, 172, 180, 170, 161]

current_position = 0, low_price = 143, high_price = 143

Current position iterations follow as outlined.

Paper For Above Instructions

The stock market presents a rich field for analysis using algorithmic strategies, which can help investors identify optimal buying and selling periods. The presented algorithm outlines a basic framework to determine the highest and lowest stock prices over a given period, facilitating informed trading decisions.

Understanding the Algorithm

This algorithm starts with an initial position, setting both the low and high prices to the stock price at that position. As it iterates through a predefined set of stock prices, it adjusts the low and high values according to the provided price at each subsequent position, enabling dynamic evaluation of market conditions.

Algorithm Step-by-Step Process

The first step is to initialize the variables. Setting the current_position to zero, along with initializing low_price and high_price to zero, provides a clean slate for calculations.

Next, as the algorithm moves through the stock prices, it compares the low_price against the current stock price:

  • If the current stock price is lower than the low_price, the algorithm updates the low_price to this new lower value.
  • If the current stock price exceeds the high_price, it updates the high_price as well.

This iterative process continues until all prices have been evaluated, ensuring that the final identified low and high prices accurately reflect the fluctuations in stock value.

Example Walkthrough

Consider the input stock prices over ten days: [143, 122, 121, 119, 147, 170, 172, 180, 170, 161]. At each step, the algorithm evaluates the current price and adjusts accordingly:

  1. At Day 1 (price = 143): low_price and high_price become 143.
  2. At Day 2 (price = 122): low_price updates to 122.
  3. At Day 3 (price = 121): low_price updates to 121.
  4. At Day 4 (price = 119): low_price updates to 119.
  5. At Day 5 (price = 147): high_price updates to 147.
  6. Continued evaluation leads us to a final high of 180 and low of 119.

Through this procedure, an investor can pinpoint buying opportunities based on low prices and potential selling points aligned with high prices. The final outcome suggests buying at 119 and selling at 180 for an ideal profit strategy.

Implications of the Algorithm for Investment Strategies

This algorithm not only provides a technical method to analyze stock prices but serves to instill a systematic approach to investment. Routines like these can significantly mitigate emotional decision-making, replacing it with data-driven strategies. In fast-paced market environments, employing such algorithms can protect and enhance capital by ensuring timely reactions to price changes.

Conclusion

In summary, the procedure of evaluating stock prices through completed steps provides essential insights into market dynamics. It is crucial for investors to develop and leverage algorithms to continually assess market conditions, ensuring a robust portfolio and strategic growth over time. This foundational example serves as a springboard into deeper academic and practical explorations of financial algorithms.

References

  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
  • Friedman, J. H., & Fisher, N. I. (1999). Bump Hunting in High-Dimensional Data. "Journal of Computational and Graphical Statistics".
  • Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
  • Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3), 425-442.
  • Sharpe, W. F. (1970). Portfolio Theory and Capital Markets. McGraw-Hill.
  • Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1), 57-82.
  • Chen, J. (2020). How to Calculate the Variance of a Portfolio. Investopedia.
  • Black, F., & Litterman, R. (1992). Global Asset Allocation with Equally-Typed Funds. Financial Analysts Journal, 48(3), 28-43.
  • Elton, E. J., Gruber, M. J., & Brown, S. J. (2003). Modern Portfolio Theory and Investment Analysis. John Wiley & Sons.
  • Malkiel, B. G. (2003). The Efficient Market Hypothesis and Its Critics. The Journal of Economic Perspectives, 17(1), 59-82.

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