# ===================================================================
# MODULE 10, LESSON 1: THE EVOLVING LANDSCAPE
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("TREND RADAR – ANALYZING THE FUTURE OF BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: DEFINE THE TREND RADAR FRAMEWORK
# ----------------------------------------------------------------
print("\n" + "-"*60)
print("PART A: Trend Radar Dashboard")
print("-"*60)

# We define trends based on two axes:
# Axis 1: Impact on Banking (Low to High) [1-5]
# Axis 2: Time to Maturity (Short, Mid, Long Term)

trends_data = {
    'Trend': [
        'Generative AI for Operations',
        'Central Bank Digital Currencies (CBDCs)',
        'Embedded Finance / BaaS',
        'Quantum Computing (Security)',
        'Decentralized Finance (Institutional)',
        'Biometric Authentication (Passkeys)',
        'Green / ESG Lending',
        'Open Data / Open Finance'
    ],
    'Impact_Score': [5, 3, 5, 4, 3, 4, 3, 5],  # 1-5
    'Time_Horizon': [1, 3, 1, 5, 2, 2, 3, 1],  # Years to mainstream
    'Risk_Factor': [3, 4, 2, 3, 5, 2, 2, 2]     # 1-5 (Implementation complexity)
}

trends_df = pd.DataFrame(trends_data)

# Calculate a Priority Score: Impact / (Time * Risk/10)
trends_df['Priority_Score'] = (trends_df['Impact_Score'] * 10) / (trends_df['Time_Horizon'] * trends_df['Risk_Factor'])
trends_df = trends_df.sort_values('Priority_Score', ascending=False)

print("Prioritized Future Trends for Banking:")
print(trends_df.to_string(index=False))
print("\n(Note: Higher Priority Score = Most Urgent & Impactful)")

# ----------------------------------------------------------------
# PART B: VISUALIZE THE TREND RADAR (BUBBLE CHART)
# ----------------------------------------------------------------
print("\n" + "-"*60)
print("PART B: Trend Radar Visualization")
print("-"*60)

# Create a bubble chart: X=Time, Y=Impact, Size=Risk, Color=Priority
plt.figure(figsize=(12, 8))
plt.scatter(
    trends_df['Time_Horizon'], 
    trends_df['Impact_Score'],
    s=trends_df['Risk_Factor'] * 100,  # Bubble size
    c=trends_df['Priority_Score'],
    cmap='viridis', 
    alpha=0.7,
    edgecolors='black'
)

# Annotate points
for i, row in trends_df.iterrows():
    plt.annotate(
        row['Trend'], 
        (row['Time_Horizon'] + 0.05, row['Impact_Score'] + 0.05),
        fontsize=8,
        ha='left'
    )

plt.xlabel('Time to Maturity (Years)')
plt.ylabel('Business Impact (1-5)')
plt.title('Future Trends Radar: Time vs. Impact')
plt.grid(True, alpha=0.3)
plt.colorbar(label='Priority Score (Higher = More Urgent)')

# Add quadrant lines for clarity
plt.axhline(y=3.5, color='red', linestyle='--', alpha=0.3)
plt.axvline(x=2.5, color='red', linestyle='--', alpha=0.3)
plt.text(4.5, 4.5, 'Long-term / High Impact', fontsize=9, alpha=0.7)
plt.text(1.5, 4.5, 'Short-term / High Impact\n(ACT NOW!)', fontsize=9, fontweight='bold', alpha=0.8)

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

# ----------------------------------------------------------------
# PART C: EXECUTIVE SUMMARY
# ----------------------------------------------------------------
print("\n" + "="*70)
print("LESSON 1 SUMMARY FOR THE DATA PRACTITIONER")
print("="*70)

print("""
1. The future is defined by Demographic, Technological, Geopolitical, and Climate forces.
2. Generative AI and Embedded Finance are immediate priorities (High Impact, Short Time).
3. BigTechs are threats due to distribution, but banks have a trust advantage.
4. As a Data Practitioner, you should prioritize data interoperability to enable BaaS.
5. Action: Use the Trend Radar code to regularly scan and re-prioritize your strategy.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 10")
print("="*70)