Merge pull request #10 from Muawiya-contact/STA

STA → Stocks Trend Analyzer

Author: Muawiya-contact

Stocks Trend Analyzer/README.md

@@ -0,0 +1,154 @@
+# 📈 Stocks Trend Analyzer (Data Structures Project)
+
+**Course:** Data Structures & Algorithms (DSA)  
+**Semester:** 3rd  
+**Submitted To:** Sir Hasnain Yousaf Khan  
+**Submitted By:**
+
+- 🧠 *Shazada M. Umar* (2k24_BSAI_42)  
+- 🧠 *Moavia Amir* (2k24_BSAI_72) — contactmuawia@gmail.com  
+- ⚙️ *Faizan Ishfaq* (2k24_BSAI_50)  
+- ⚙️ *M. Hamza* (2k24_BSAI_46)
+
+---
+
+## 📘 Project Overview
+
+**Stocks Trend Analyzer** is a Python-based application that analyzes historical stock market data using **Data Structures and Algorithms**.  
+The system detects rising/falling patterns, computes moving averages, highlights buy–sell intervals, and visualizes trends using **Tkinter** and **Matplotlib**.
+
+The goal is to help students understand how DSA concepts apply to real-world financial time-series data.
+
+---
+
+## 🔍 Problem Statement
+
+Financial datasets are large, noisy, and difficult for beginners to interpret. Without proper analysis, it becomes challenging to identify:
+
+- Continuous rising or falling trends  
+- Local highs and lows  
+- Smoothed patterns (moving averages)  
+- Optimal moments to buy or sell  
+
+This project provides a simple and visual solution using efficient algorithms and Python-based tools.
+
+---
+
+## 🎯 Objectives
+
+- Build a working desktop tool to analyze stock time-series data  
+- Implement DSA concepts such as arrays, queues, stacks, and maps  
+- Detect trends and compute moving averages  
+- Identify the best buy–sell intervals using an O(n) algorithm  
+- Visualize insights using charts  
+- Create a user-friendly GUI for easy interaction  
+
+---
+
+## 🧠 System Overview
+
+| Component | Purpose |
+|----------|----------|
+| **Python (Core)** | Implements algorithms and data structures |
+| **Tkinter** | GUI for importing and analyzing data |
+| **Matplotlib** | Visualization of trends and signals |
+| **CSV Dataset** | Yahoo Finance or Kaggle stock data |
+| **DSA Used** | Arrays, Lists, Stacks, Queues, Maps |
+
+---
+
+## 🔬 Working Principle
+
+1. User loads a CSV file containing daily stock prices.  
+2. Data is stored in custom **Array/List** structures.  
+3. Algorithms run on the dataset:  
+   - Trend detection  
+   - Moving average calculation (Queue / Sliding Window)  
+   - Local high/low using Stack logic  
+   - Max-profit interval (Buy–Sell Strategy)  
+4. Tkinter displays results and summary.  
+5. Matplotlib generates charts with lines, markers, and trends.
+
+---
+
+## 🛠 Features
+
+- 📊 **Trend Detection:** Identifies rising/falling streaks  
+- 📉 **Moving Averages:** Smooths noisy data using queue-based windows  
+- 💹 **Best Buy–Sell Interval:** Shows most profitable days  
+- 📈 **Visual Charts:** Price lines, moving averages, highs/lows  
+- 🖥 **User-Friendly GUI:** Import CSV and analyze instantly  
+- 🧠 **DSA-Focused Implementation:** Each module uses required structures  
+
+---
+
+## 🛠 Technologies & Tools
+
+- Python  
+- Tkinter (GUI)  
+- Matplotlib (Visualization)  
+- Custom Data Structures  
+- CSV Data (Stock Prices)
+
+---
+
+## 📂 Folder Structure
+
+```
+Stocks-Trend-Analyzer/
+├─ README.md             # (this file)
+├─ main.py               # Tkinter GUI
+├─ algorithms/
+│  ├─ trend_analysis.py
+│  ├─ moving_average.py
+│  ├─ max_profit.py
+│  └─ local_high_low.py
+├─ dsa/
+│  ├─ array_list.py
+│  ├─ queue.py
+│  └─ stack.py
+├─ data/
+│  └─ sample_stock.csv
+├─ charts/
+│  └─ generated_charts.png
+└─ docs/
+   ├─ Proposal.pdf
+   └─ Report.pdf
+```
+---
+
+## 📅 Project Timeline (6 Weeks)
+
+| Week | Task |
+|------|------|
+| Week 1 | Requirement analysis, dataset collection |
+| Week 2 | Implement data structures |
+| Week 3 | Implement algorithms |
+| Week 4 | Develop Tkinter GUI |
+| Week 5 | Add Matplotlib charts |
+| Week 6 | Testing, debugging, documentation |
+
+```
+
+---
+
+## 🎓 Expected Learning Outcomes
+
+- Understand and apply DSA to financial datasets  
+- Learn how queues, stacks, and arrays operate in practical use-cases  
+- Gain experience with GUI development  
+- Learn data visualization techniques  
+- Strengthen algorithmic thinking and problem-solving  
+
+---
+
+## 🧾 Conclusion
+
+The **Stocks Trend Analyzer** blends algorithms, data structures, and visualization to provide meaningful insights into stock behavior.  
+This project demonstrates the power of DSA in solving real-world problems and sets a foundation for advanced financial analytics, predictive modeling, and multi-stock comparison systems.
+
+---
+
+## 📫 Contact
+
+- **Moavia Amir** — contactmuawia@gmail.com  

Stocks Trend Analyzer/algorithms/__pycache__/local_high_low.cpython-312.pyc

@@ -0,0 +1,18 @@
+from dsa.stack import Stack
+
+class LocalHighLow:
+    def __init__(self, prices):
+        self.prices = prices
+    
+    def find_local_extremes(self):
+        stack = Stack()
+        extremes = []
+        n = len(self.prices)
+        for i in range(n):
+            if i == 0 or i == n-1:
+                continue
+            if self.prices[i] > self.prices[i-1] and self.prices[i] > self.prices[i+1]:
+                extremes.append((i, "High", self.prices[i]))
+            elif self.prices[i] < self.prices[i-1] and self.prices[i] < self.prices[i+1]:
+                extremes.append((i, "Low", self.prices[i]))
+        return extremes

Stocks Trend Analyzer/algorithms/__pycache__/max_profit.cpython-312.pyc

@@ -0,0 +1,23 @@
+class MaxProfit:
+    def __init__(self, prices):
+        self.prices = prices
+    
+    def best_interval(self):
+        if not self.prices:
+            return None, None, 0
+        
+        min_price = self.prices[0]
+        min_index = 0
+        max_profit = 0
+        buy_day = 0
+        sell_day = 0
+        
+        for i, price in enumerate(self.prices):
+            if price - min_price > max_profit:
+                max_profit = price - min_price
+                buy_day = min_index
+                sell_day = i
+            if price < min_price:
+                min_price = price
+                min_index = i
+        return buy_day, sell_day, max_profit

Stocks Trend Analyzer/algorithms/__pycache__/moving_average.cpython-312.pyc

@@ -0,0 +1,15 @@
+from dsa.queue import Queue
+
+class MovingAverage:
+    def __init__(self, window_size):
+        self.window_size = window_size
+    
+    def calculate(self, prices):
+        q = Queue()
+        moving_avgs = []
+        for price in prices:
+            q.enqueue(price)
+            if q.size() > self.window_size:
+                q.dequeue()
+            moving_avgs.append(q.average())
+        return moving_avgs

Stocks Trend Analyzer/algorithms/__pycache__/trend_analysis.cpython-312.pyc

@@ -0,0 +1,23 @@
+from dsa.array_list import ArrayList
+
+class TrendAnalysis:
+    def __init__(self, prices):
+        # prices: ArrayList of closing prices
+        self.prices = prices
+    
+    def detect_trends(self):
+        trends = ArrayList()
+        n = self.prices.size()
+        if n == 0:
+            return trends
+        
+        streak = 1
+        for i in range(1, n):
+            if self.prices.get(i) > self.prices.get(i-1):
+                streak = streak + 1 if streak > 0 else 1
+            elif self.prices.get(i) < self.prices.get(i-1):
+                streak = streak - 1 if streak < 0 else -1
+            else:
+                streak = 0  # no change
+            trends.append(streak)
+        return trends