┌─────────────────────────────────────────────────────────────────────────────┐
│                    RESEARCH APPROACHES                                     │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  QUANTITATIVE RESEARCH                                                     │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Numeric data                                                      │   │
│  │ • Statistical analysis                                              │   │
│  │ • Objective and structured                                         │   │
│  │ • Examples: On-chain data analysis, token velocity calculation      │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  QUALITATIVE RESEARCH                                                      │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Non-numeric data                                                  │   │
│  │ • Interpretive analysis                                             │   │
│  │ • Subjective and unstructured                                       │   │
│  │ • Examples: Case studies, regulatory analysis, community sentiment  │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  MIXED METHODS                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Combines quantitative and qualitative                            │   │
│  │ • Comprehensive understanding                                       │   │
│  │ • Examples: Protocol analysis with community feedback               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 10, LESSON 3: RESEARCH AND DATA COLLECTION
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import random
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("RESEARCH AND DATA COLLECTION")
print("="*70)

# ----------------------------------------------------------------
# PART A: DATA COLLECTION SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Data Collection Simulation")
print("-"*60)

def simulate_blockchain_data(days: int = 365) -> pd.DataFrame:
    """
    Simulate blockchain data for research purposes.
    """
    start_date = datetime.now() - timedelta(days=days)
    dates = [start_date + timedelta(days=i) for i in range(days)]
    
    # Simulate price data with trend and volatility
    price = 1000  # Starting price
    prices = []
    volumes = []
    active_addresses = []
    transaction_counts = []
    
    for i in range(days):
        # Random walk with drift
        trend = 0.001 if i < days * 0.6 else -0.0005
        noise = np.random.normal(0, 0.02)
        price = price * (1 + trend + noise)
        prices.append(max(1, price))
        
        # Volume correlates with volatility
        volume = abs(noise) * price * 10000 + random.uniform(0, 500000)
        volumes.append(volume)
        
        # Active addresses grow over time
        base = 1000 + i * 10
        active_addresses.append(base + np.random.normal(0, 200))
        
        # Transaction count correlates with active addresses
        transactions = active_addresses[-1] * random.uniform(0.2, 0.5)
        transaction_counts.append(transactions)
    
    return pd.DataFrame({
        'date': dates,
        'price': prices,
        'volume': volumes,
        'active_addresses': active_addresses,
        'transactions': transaction_counts
    })

# Collect data
print("Simulating blockchain data...")
df = simulate_blockchain_data(365)
print(f"Data collected: {len(df)} days")
print("\nSample Data:")
print(df.head(10).to_string(index=False))

# ----------------------------------------------------------------
# PART B: DATA ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Data Analysis")
print("-"*60)

class BlockchainAnalytics:
    """
    Analytical tools for blockchain data.
    """
    def __init__(self, data: pd.DataFrame):
        self.data = data
    
    def descriptive_stats(self) -> pd.DataFrame:
        """Calculate descriptive statistics."""
        return self.data.select_dtypes(include=[np.number]).describe()
    
    def calculate_metrics(self) -> Dict:
        """Calculate key metrics."""
        metrics = {
            'avg_price': self.data['price'].mean(),
            'median_price': self.data['price'].median(),
            'price_volatility': self.data['price'].std() / self.data['price'].mean(),
            'avg_volume': self.data['volume'].mean(),
            'peak_price': self.data['price'].max(),
            'peak_price_date': self.data.loc[self.data['price'].idxmax(), 'date'],
            'total_volume': self.data['volume'].sum(),
            'growth_rate': (self.data['price'].iloc[-1] / self.data['price'].iloc[0]) - 1
        }
        return metrics
    
    def correlation_analysis(self) -> pd.DataFrame:
        """Calculate correlations between numeric variables."""
        return self.data.select_dtypes(include=[np.number]).corr()
    
    def calculate_rolling_metrics(self, window: int = 30) -> pd.DataFrame:
        """Calculate rolling averages."""
        df = self.data.copy()
        df['price_ma'] = df['price'].rolling(window).mean()
        df['price_std'] = df['price'].rolling(window).std()
        df['volume_ma'] = df['volume'].rolling(window).mean()
        return df

# Analyse data
analytics = BlockchainAnalytics(df)

print("Descriptive Statistics:")
print(analytics.descriptive_stats().round(2))

print("\nKey Metrics:")
metrics = analytics.calculate_metrics()
for key, value in metrics.items():
    if isinstance(value, datetime):
        print(f"  {key}: {value.strftime('%Y-%m-%d')}")
    elif isinstance(value, float):
        print(f"  {key}: {value:.2f}")
    else:
        print(f"  {key}: {value}")

print("\nCorrelation Analysis:")
print(analytics.correlation_analysis().round(2))

# ----------------------------------------------------------------
# PART C: DATA VISUALISATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Data Visualisation")
print("-"*60)

# Create visualisations
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# 1. Price over time
ax1 = axes[0, 0]
ax1.plot(df['date'], df['price'], color='blue', linewidth=2)
ax1.set_xlabel('Date')
ax1.set_ylabel('Price')
ax1.set_title('Price Over Time')
ax1.grid(True, alpha=0.3)

# 2. Volume over time
ax2 = axes[0, 1]
ax2.bar(df['date'], df['volume'], color='orange', alpha=0.7)
ax2.set_xlabel('Date')
ax2.set_ylabel('Volume')
ax2.set_title('Trading Volume Over Time')
ax2.grid(True, alpha=0.3)

# 3. Price vs Active Addresses
ax3 = axes[1, 0]
ax3.scatter(df['active_addresses'], df['price'], alpha=0.5, color='green')
ax3.set_xlabel('Active Addresses')
ax3.set_ylabel('Price')
ax3.set_title('Price vs Active Addresses')
ax3.grid(True, alpha=0.3)

# 4. Rolling metrics
rolling_df = analytics.calculate_rolling_metrics(30)
ax4 = axes[1, 1]
ax4.plot(rolling_df['date'], rolling_df['price'], label='Price', color='blue', alpha=0.5)
ax4.plot(rolling_df['date'], rolling_df['price_ma'], label='30-day MA', color='red', linewidth=2)
ax4.set_xlabel('Date')
ax4.set_ylabel('Price')
ax4.set_title('Price with Moving Average')
ax4.legend()
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('research_data_visualisation.png', dpi=300, bbox_inches='tight')
plt.show()
print("Data visualisation saved as 'research_data_visualisation.png'")

# ----------------------------------------------------------------
# PART D: RESEARCH METHODS FRAMEWORK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Research Methods Framework")
print("-"*60)

research_framework = {
    "Step 1: Define Research Question": {
        "Questions": [
            "What problem are you investigating?",
            "What is the scope of the research?",
            "What is the expected outcome?"
        ],
        "Example": "How does token velocity affect price in DeFi protocols?"
    },
    "Step 2: Literature Review": {
        "Actions": [
            "Review existing research",
            "Identify gaps in literature",
            "Build theoretical framework"
        ],
        "Sources": ["Academic databases", "Industry reports", "White papers"]
    },
    "Step 3: Data Collection": {
        "Methods": [
            "On-chain data collection",
            "Off-chain data collection",
            "Surveys/interviews"
        ],
        "Tools": ["Dune Analytics", "CoinGecko", "Twitter API"]
    },
    "Step 4: Data Analysis": {
        "Methods": [
            "Quantitative analysis",
            "Qualitative analysis",
            "Mixed methods"
        ],
        "Tools": ["Python", "R", "Excel", "SPSS"]
    },
    "Step 5: Interpretation": {
        "Actions": [
            "Draw conclusions",
            "Identify patterns",
            "Explain findings"
        ],
        "Outputs": ["Insights", "Recommendations"]
    }
}

for step, details in research_framework.items():
    print(f"\n{step.upper()}:")
    for key, value in details.items():
        if isinstance(value, list):
            print(f"  {key}:")
            for item in value:
                print(f"    • {item}")
        else:
            print(f"  {key}: {value}")

# ----------------------------------------------------------------
# PART E: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART E: Summary and Recommendations")
print("="*70)

print("""
Research and Data Collection – Key Takeaways:

1. Research types: exploratory, descriptive, explanatory, evaluative, predictive.
2. Approaches: quantitative (numeric), qualitative (interpretive), mixed methods.
3. Data sources: on-chain (Dune, The Graph, Glassnode), off-chain (CoinGecko, Twitter, GitHub).
4. Quantitative techniques: descriptive stats, time series, correlation, regression, network analysis.
5. Qualitative techniques: thematic analysis, content analysis, case study, comparative analysis.
6. Tools: Python, Pandas, Matplotlib, SQL, R, Jupyter.

Research Checklist:
  - Define clear research questions.
  - Review existing literature.
  - Select appropriate methodology.
  - Collect reliable data.
  - Use appropriate analysis techniques.
  - Interpret findings carefully.
  - Document all steps thoroughly.

Recommendations:
  - Use multiple data sources for triangulation.
  - Document data collection methods.
  - Handle missing data appropriately.
  - Visualise data for insights.
  - Interpret results within limitations.
  - Cite all sources properly.
""")