Data Structure and Algorithms- Arrays-Stock Price analysis

 


Common Data Structures and Algorithms

Data Structures

  • Arrays: Ordered collection of elements with fixed size.
  • Linked Lists: Dynamic collection of elements linked together.
  • Stacks: LIFO (Last-In-First-Out) data structure.
  • Queues: FIFO (First-In-First-Out) data structure.
  • Trees: Hierarchical data structure with nodes and edges.
    • Binary Search Trees
    • AVL Trees
    • B-Trees
  • Graphs: Collection of nodes (vertices) connected by edges.
    • Directed Graphs
    • Undirected Graphs
  • Hash Tables: Unordered collection of key-value pairs.

Algorithms

  • Searching:
    • Linear Search
    • Binary Search
  • Sorting:
    • Bubble Sort
    • Insertion Sort
    • Selection Sort
    • Merge Sort
    • Quick Sort  
    • Heap Sort
  • Graph Algorithms:
    • Breadth-First Search (BFS)
    • Depth-First Search (DFS)
    • Dijkstra's Algorithm
    • Bellman-Ford Algorithm
    • Floyd-Warshall Algorithm  
  • Dynamic Programming:
    • Fibonacci Sequence
    • Longest Common Subsequence
    • Knapsack Problem
  • Greedy Algorithms:
    • Activity Selection Problem
    • Fractional Knapsack Problem
  • Divide and Conquer:
    • Merge Sort
    • Quick Sort
    • Closest Pair of Points

Additional Data Structures and Algorithms:

  • Tries: Efficient data structure for string searching.
  • Heaps: Priority queues based on complete binary trees.
  • Disjoint Sets: Data structure for maintaining disjoint sets.
  • Suffix Trees: Data structures for efficient substring search.
  • K-d Trees: Space-partitioning data structure for efficient nearest neighbor search.


Arrays:
---------

1. Image Processing:

  • Pixel Manipulation: Create a program to manipulate the pixels of an image (e.g., invert colors, rotate, apply filters).
  • Image Filtering: Implement filters like Gaussian blur, edge detection, or sharpening using array operations.
  • Image Compression: Explore image compression techniques like JPEG or PNG using array-based algorithms.

2. Data Analysis:

  • Statistical Calculations: Calculate mean, median, mode, standard deviation, and variance of a dataset stored in a NumPy array.
  • Data Visualization: Use libraries like Matplotlib or Seaborn to create visualizations (e.g., histograms, scatter plots, line charts) based on array data.
  • Data Mining: Implement algorithms like k-means clustering or principal component analysis to analyze large datasets.

3. Machine Learning:

  • Feature Engineering: Extract relevant features from raw data stored in arrays (e.g., one-hot encoding, normalization).
  • Model Training: Train machine learning models (e.g., linear regression, decision trees, neural networks) using array-based data.
  • Model Evaluation: Evaluate the performance of machine learning models using metrics calculated from arrays (e.g., accuracy, precision, recall).

4. Scientific Computing:

  • Numerical Simulations: Simulate physical phenomena (e.g., fluid dynamics, heat transfer) using numerical methods and array operations.
  • Scientific Visualization: Create visualizations of scientific data using libraries like Matplotlib or Mayavi.
  • Data Analysis: Analyze scientific data (e.g., experimental results, sensor readings) using array-based techniques.

5. Game Development:

  • Game Objects: Represent game objects (e.g., players, enemies, projectiles) as arrays of properties.
  • Collision Detection: Implement collision detection algorithms using array-based techniques.
  • Physics Simulations: Simulate game physics (e.g., gravity, movement) using array-based calculations.

6. Financial Analysis:

  • Stock Price Analysis: Analyze stock price data stored in arrays to identify trends and patterns.
  • Portfolio Optimization: Optimize investment portfolios using array-based optimization techniques.
  • Risk Assessment: Calculate risk metrics (e.g., volatility, correlation) for financial data stored in arrays.
==

Python Assignment: Stock Price Analysis Using Arrays

Problem: Analyze a dataset of historical stock prices to identify trends, patterns, and calculate key metrics.

Data: You can use real-world stock price data from financial APIs like Yahoo Finance, Alpha Vantage, or Google Finance. Alternatively, you can create a synthetic dataset for practice.


Tasks:

  1. Data Loading and Cleaning:

    • Load the stock price data into a NumPy array.
    • Handle missing values or outliers if necessary.

  2. Basic Calculations:

    • Calculate the daily returns (percentage change in price) using array operations.
    • Calculate the cumulative returns over a specific period.
    • Calculate the average, median, and standard deviation of the daily returns.

  3. Trend Analysis:

    • Use moving averages (simple, exponential, or weighted) to identify trends in the stock price.
    • Calculate the momentum of the stock price.
    • Detect support and resistance levels using technical analysis techniques.

  4. Volatility Analysis:

    • Calculate the volatility (standard deviation of returns) over different time periods.
    • Use Bollinger Bands to identify overbought or oversold conditions.

  5. Correlation Analysis:

    • Calculate the correlation between the stock's returns and a benchmark index (e.g., S&P 500).
    • Analyze the correlation with other stocks in the same sector.

  6. Visualization:

    • Use Matplotlib or Seaborn to create visualizations like line charts, histograms, scatter plots, and candlestick charts.
    • Visualize the stock price, returns, trends, and volatility.

    • sample stock_data.csv

Date,Open,High,Low,Close,Volume
2023-01-01,150,155,149,153,1000000
2023-01-02,153,158,152,157,1200000
2023-01-03,157,160,155,158,1100000
2023-01-04,158,162,157,161,1300000
2023-01-05,161,165,160,164,1400000
2023-01-06,164,168,163,167,1500000
2023-01-07,167,170,166,169,1600000
2023-01-08,169,172,168,171,1700000
2023-01-09,171,175,170,174,1800000
2023-01-10,174,178,173,177,1900000


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load stock price data from a CSV file
data = pd.read_csv('stock_data.csv')

# Extract closing prices
close_prices = data['Close'].values

# Calculate daily returns
returns = (close_prices[1:] - close_prices[:-1]) / close_prices[:-1]

# Calculate cumulative returns
cumulative_returns = (1 + returns).cumprod() - 1

# Calculate moving averages
short_term_ma = np.convolve(close_prices, np.ones(5) / 5, mode='valid')
long_term_ma = np.convolve(close_prices, np.ones(20) / 20, mode='valid')

# Calculate average, median, and standard deviation of daily returns
average_return = np.mean(returns)
median_return = np.median(returns)
std_dev_return = np.std(returns)

# Calculate volatility (standard deviation of returns) over different time periods
volatility = std_dev_return

# Calculate Bollinger Bands
rolling_mean = pd.Series(close_prices).rolling(window=20).mean()
rolling_std = pd.Series(close_prices).rolling(window=20).std()
upper_band = rolling_mean + (rolling_std * 2)
lower_band = rolling_mean - (rolling_std * 2)

# Visualization
plt.figure(figsize=(14, 7))

# Plot stock price and moving averages
plt.subplot(2, 2, 1)
plt.plot(close_prices, label='Close Prices')
plt.plot(range(4, len(short_term_ma) + 4), short_term_ma, label='5-day MA')
plt.plot(range(19, len(long_term_ma) + 19), long_term_ma, label='20-day MA')
plt.title('Stock Price and Moving Averages')
plt.legend()

# Plot daily returns
plt.subplot(2, 2, 2)
plt.hist(returns, bins=50, alpha=0.75)
plt.title('Histogram of Daily Returns')

# Plot Bollinger Bands
plt.subplot(2, 2, 3)
plt.plot(close_prices, label='Close Prices')
plt.plot(rolling_mean, label='20-day MA')
plt.plot(upper_band, label='Upper Band')
plt.plot(lower_band, label='Lower Band')
plt.fill_between(range(len(close_prices)), upper_band, lower_band, color='gray', alpha=0.2)
plt.title('Bollinger Bands')
plt.legend()

# Plot cumulative returns
plt.subplot(2, 2, 4)
plt.plot(cumulative_returns, label='Cumulative Returns')
plt.title('Cumulative Returns')
plt.legend()

plt.tight_layout()
plt.show()

# Print calculated metrics
print(f'Average Daily Return: {average_return:.4f}')
print(f'Median Daily Return: {median_return:.4f}')
print(f'Standard Deviation of Daily Returns: {std_dev_return:.4f}')
print(f'Volatility: {volatility:.4f}')

Comments

Post a Comment

Popular posts from this blog

AI Agents for Enterprise Leaders -Next Era of Organizational Transformation

  AI Agents for Enterprise Leaders: Charting a Course into the Next Era of Organizational Transformation Introduction AI agents and multiagent AI systems represent more than just technological advancements. They signify a fundamental shift in how organizations can automate processes, improve human-machine collaboration, generate insights, and respond dynamically to complex challenges. These systems offer the potential to unlock significant value across a wide range of functions—from enhancing customer interactions and optimizing supply chains to driving innovation in product development and service delivery. Realizing the Benefits To realize these benefits, organizations must engage in deliberate planning, make strategic investments, and foster a culture of continuous improvement and technological advancement. By aligning AI agent initiatives with core business goals, investing in the right infrastructure, and nurturing a culture of innovation, enterprises can position themselves t...
Read more

Airport twin basic requirements

  1. 3D Model of  New Terminal Arrivals Area: Develop a high-fidelity 3D model of the New Terminal Arrivals Area using provided LiDAR/CAD data and images. Include key elements like baggage carousels, immigration counters, customs checkpoints, and waiting areas. 2. Real-time Passenger Flow Monitoring: Integrate with Xovis and CCTV systems to track passenger movement in real-time. Visualize passenger flow on the 3D model, highlighting congestion areas and potential bottlenecks. Display real-time passenger count and density information on dashboards. 3. Baggage Handling Visualization: Integrate with the baggage handling system to track baggage movement in real-time. Visualize baggage flow on the 3D model, showing baggage movement from aircraft to carousels. Display real-time baggage status and potential delays on dashboards. 4. Security Monitoring: Integrate with CCTV feeds to monitor the Arrivals Area for suspicious activities. Implement AI-powered video analytics f...
Read more

The AI Revolution: Are You Ready? my speech text in multiple languages -Hindi,Arabic,Malayalam,English

  The AI Revolution: Are You Ready?  https://www.linkedin.com/company/105947510 CertifAI Labs My Speech text on Future of Tomorrow in English, Arabic ,Hindi and Malayalam , All translations done by Gemini LLM "Imagine a world with self-writing software, robots working alongside us, and doctors with instant access to all the world's medical information. This isn't science fiction, friends; this is the world AI is building right now. The future isn't a distant dream, but a wave crashing upon our shores, rapidly transforming the job landscape. The question isn't if this change will happen, but how we will adapt to it." "Think about how we create. For generations, software development was a complex art mastered by a select few. But what if anyone with an idea and a voice could bring that idea to life? What if a child could build a virtual solar system in minutes, simply by asking? We're moving towards a world where computers speak our language, paving the...
Read more