import warnings
import yfinance as yf
import pandas as pd

# Suppress annoying FutureWarnings
warnings.simplefilter(action='ignore', category=FutureWarning)

# 1. Define Tickers (Ordered alphabetically for easy renaming)
tickers = ["BTC-USD", "DX-Y.NYB", "USDC-USD", "XRP-USD"]

# 2. Download Data
data = yf.download(tickers, start="2019-01-01", end="2024-05-01", progress=False)

# 3. Clean and Extract 'Close' prices
# auto_adjust=True (default) makes 'Close' the final adjusted price
prices = data['Close'].dropna()

# 4. Normalize to 100
normalized = (prices / prices.iloc[0]) * 100

# 5. Rename Columns accurately (matching alphabetical order)
normalized.columns = ['Bitcoin (BTC)', 'US Dollar Index', 'USD Coin (USDC)', 'XRP Performance']

print("Success! Multi-Asset Performance normalized to 100:")
display(normalized.head())

Success! Multi-Asset Performance normalized to 100:

import plotly.express as px
import plotly.io as pio

# Use 'html' to ensure the code is embedded directly into the notebook cells
pio.renderers.default = "notebook_connected"

# 1. CREATE THE INTERACTIVE PLOT
# Use px.line with your normalized dataframe
# x=normalized.index uses your Date column
fig = px.line(
    normalized, 
    x=normalized.index, 
    y=normalized.columns,
    title='Asset Performance Comparison (Baseline: Jan 2019 = 100)',
    labels={'value': 'Normalized Price (100 = Start)', 'variable': 'Asset'},
    template='plotly_dark'  # Matches your site's "Matrix" dark theme
)

# 2. CUSTOMIZE THE STYLE
fig.update_layout(
    font_family="IBM Plex Sans",
    hovermode="x unified",  # Shows all asset values in one tooltip when hovering
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    yaxis_title="Normalized Value (%)",
    xaxis_title="Timeline"
)

# 3. EMPHASIZE KEY LINES (Optional)
# Make crypto lines thicker and the Dollar/USDC thinner for contrast
fig.update_traces(line=dict(width=1.5), selector=dict(name='US Dollar Index'))
fig.update_traces(line=dict(width=1.5), selector=dict(name='USD Coin (USDC)'))
fig.update_traces(line=dict(width=3), selector=dict(name='XRP Performance'))

fig.show()

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# --- A. CALCULATE ROLLING CORRELATION (90-Day) ---
# This shows how XRP's relationship with BTC "tightens" or "decouples"
correlation_series = prices['XRP-USD'].rolling(window=90).corr(prices['BTC-USD'])

# --- B. CALCULATE VOLATILITY (30-Day Rolling Standard Deviation) ---
volatility = prices.pct_change().rolling(window=30).std() * (252**0.5) * 100 

# --- C. PLOT: CORRELATION & VOLATILITY DASHBOARD ---
fig_metrics = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                          vertical_spacing=0.1,
                          subplot_titles=("90-Day Price Correlation: XRP vs. BTC", 
                                          "30-Day Annualized Volatility (%)"))

# Add Correlation Trace
fig_metrics.add_trace(go.Scatter(x=correlation_series.index, y=correlation_series, 
                               name="XRP/BTC Correlation", line=dict(color='#3fb950')), row=1, col=1)

# Add Volatility Traces
fig_metrics.add_trace(go.Scatter(x=volatility.index, y=volatility['XRP-USD'], 
                               name="XRP Volatility", line=dict(color='#FFD700')), row=2, col=1)
fig_metrics.add_trace(go.Scatter(x=volatility.index, y=volatility['BTC-USD'], 
                               name="BTC Volatility", line=dict(color='#f0b429', dash='dot')), row=2, col=1)

fig_metrics.update_layout(height=700, template='plotly_dark', showlegend=True,
                         title_text="Advanced Market Dynamics: XRP Intelligence")
fig_metrics.show(renderer='notebook_connected')

from IPython.display import HTML

# --- DYNAMIC CALCULATIONS ---
# We use .iloc[-1] to get the most recent data point vs the start (100)
total_xrp_gain = round(normalized['XRP Performance'].iloc[-1] - 100, 1)
total_btc_gain = round(normalized['Bitcoin (BTC)'].iloc[-1] - 100, 1)
dxy_change = round(normalized['US Dollar Index'].iloc[-1] - 100, 1)
avg_corr = round(correlation_series.mean(), 2)

# --- THE EXECUTIVE HTML/CSS ---
exec_summary_html = f"""
<style>
    .crypto-exec {{ background:#0d1117; color:#c9d1d9; font-family:sans-serif; padding:35px; border-radius:15px; border:1px solid #3fb950; max-width:950px; margin:20px auto; box-shadow: 0 10px 30px rgba(0,0,0,0.5); }}
    .crypto-exec h1 {{ color:#fff; font-size:24px; border-bottom: 2px solid #3fb950; padding-bottom:10px; margin-top:0; }}
    .kpi-row {{ display:flex; justify-content:space-between; margin:25px 0; gap:15px; }}
    .kpi-card {{ background:#161b22; padding:20px; border-radius:10px; flex:1; text-align:center; border:1px solid #21262d; }}
    .kpi-card .label {{ font-size:11px; text-transform:uppercase; color:#8b949e; letter-spacing:1px; }}
    .kpi-card .value {{ font-size:22px; font-weight:700; margin-top:8px; color:#3fb950; }}
    .kpi-card.alt .value {{ color:#FFD700; }}
    .insight-box {{ background:rgba(63, 185, 80, 0.1); border-left:4px solid #3fb950; padding:15px; margin-top:15px; border-radius: 0 8px 8px 0; font-size:14px; line-height:1.6; }}
    .highlight {{ color:#FFD700; font-weight:600; }}
</style>

<div class='crypto-exec'>
    <h1>XRP Performance Intelligence Briefing</h1>
    <p>Comparative analysis of <b>XRP</b>, <b>Bitcoin</b>, and the <b>US Dollar Index (DXY)</b> from 2019 to Present.</p>
    
    <div class='kpi-row'>
        <div class='kpi-card'><div class='label'>XRP Total Return</div><div class='value'>{total_xrp_gain:+,}%</div></div>
        <div class='kpi-card alt'><div class='label'>BTC Total Return</div><div class='value'>{total_btc_gain:+,}%</div></div>
        <div class='kpi-card'><div class='label'>USD Index Delta</div><div class='value'>{dxy_change:+,}%</div></div>
        <div class='kpi-card alt'><div class='label'>Avg BTC Correlation</div><div class='value'>{avg_corr}</div></div>
    </div>

    <div class='insight-box'>
        <b>Market Coupling:</b> XRP maintains an average 90-day correlation of <span class='highlight'>{avg_corr}</span> with Bitcoin. While it largely follows the broader crypto market, significant "decoupling" events align with specific legal and regulatory milestones.
    </div>
    <div class='insight-box' style='border-left-color:#FFD700;'>
        <b>Macro Divergence:</b> Since 2019, XRP's performance of <span class='highlight'>{total_xrp_gain}%</span> stands in stark contrast to the US Dollar Index move of <span class='highlight'>{dxy_change}%</span>, illustrating its role as a high-volatility "Risk-On" asset.
    </div>
</div>
"""

display(HTML(exec_summary_html))

from IPython.display import HTML

# --- DATA FOR THE MODELER ---
circ_supply = 60_900_000_000 
scenarios = [
    {"name": "Stalled Legislation (Status Quo)", "price": 1.41, "class": "warn"},
    {"name": "Clarity Act: Conservative Passage", "price": 3.50, "class": "good"},
    {"name": "Clarity Act: Moderate Adoption", "price": 5.00, "class": "good"},
    {"name": "Clarity Act: Full Institutional Inflow", "price": 8.00, "class": "ultra"}
]

# Build the scenario rows
rows_html = ""
for s in scenarios:
    m_cap = (s['price'] * circ_supply) / 1e9
    rows_html += f"""
    <div class='scenario-row {s['class']}'>
        <div class='scen-name'>{s['name']}</div>
        <div class='scen-price'>${s['price']:.2f}</div>
        <div class='scen-cap'>${m_cap:.1f}B Market Cap</div>
    </div>
    """

# --- THE STYLED CARD ---
modeller_html = f"""
<style>
    .modeller-card {{ background:#0d1117; color:#c9d1d9; font-family:sans-serif; padding:30px; border-radius:15px; border:1px solid #FFD700; max-width:950px; margin:30px auto; }}
    .modeller-card h2 {{ color:#FFD700; margin-top:0; font-size:20px; text-transform:uppercase; letter-spacing:1px; }}
    .modeller-sub {{ font-size:13px; color:#8b949e; margin-bottom:20px; border-left: 2px solid #FFD700; padding-left:10px; }}
    .scenario-grid {{ display:flex; flex-direction:column; gap:10px; }}
    .scenario-row {{ display:flex; justify-content:space-between; align-items:center; background:#161b22; padding:15px 25px; border-radius:8px; border:1px solid #21262d; transition: 0.3s; }}
    .scenario-row:hover {{ border-color:#FFD700; transform: scale(1.01); background:#1c2128; }}
    .scen-name {{ flex:2; font-weight:600; font-size:15px; }}
    .scen-price {{ flex:1; text-align:center; font-family:monospace; font-size:18px; color:#FFD700; }}
    .scen-cap {{ flex:1.5; text-align:right; font-size:12px; color:#8b949e; text-transform:uppercase; }}
    
    .scenario-row.good {{ border-left: 4px solid #3fb950; }}
    .scenario-row.warn {{ border-left: 4px solid #f0b429; }}
    .scenario-row.ultra {{ border-left: 4px solid #3fb950; box-shadow: inset 0 0 10px rgba(63,185,80,0.1); }}
    .scenario-row.ultra .scen-price {{ color:#3fb950; text-shadow: 0 0 5px rgba(63,185,80,0.5); }}
    
    .bonus-note {{ margin-top:20px; padding:15px; background:rgba(255, 215, 0, 0.05); border-radius:8px; font-size:13px; line-height:1.5; }}
</style>

<div class='modeller-card'>
    <h2>Predictive Valuation: The Clarity Act (May 2026)</h2>
    <p class='modeller-sub'>Projected valuation based on legislative outcomes for the Senate Banking Committee markup.</p>
    
    <div class='scenario-grid'>
        {rows_html}
    </div>

    <div class='bonus-note'>
        <b>Analyst Sweetener:</b> While current price action reflects legislative stalling, <b>Standard Chartered</b> maintains a price target of <span style='color:#3fb950;'>$8.00</span> contingent on a clean bill passage. A "Full Adoption" scenario places XRP in a market-cap tier comparable to Ethereum ($490B+), assuming codified institutional on-ramps.
    </div>
</div>
"""

display(HTML(modeller_html))

	Bitcoin (BTC)	US Dollar Index	USD Coin (USDC)	XRP Performance
Date
2019-01-02	100.000000	100.000000	100.000000	100.000000
2019-01-03	97.295026	99.473247	99.548604	95.997527
2019-01-04	97.826959	99.349311	99.016573	95.070926
2019-01-07	102.075333	98.853542	98.741373	97.096282
2019-01-08	102.217332	99.049785	98.992806	97.354243