macro_indice_engine.py

""" MACRO ISM (Institute for Supply Management) de la FRED (Federal Reserve Bank of St. Louis)
    ==========================================================================================
    Indice synthétique de santé économique, à partir des séries disponibles sur FRED (sources : OCDE / Eurostat / BCE).

    Pour créer une clef API FRED :
    - https://fred.stlouisfed.org/docs/api/api_key.html

    Cette clef est à placer dans le fichier :
    - C:\\Users\\{Nom d'utilisateur}\\AppData\\Local\\TradingInPython\\config.env

    sous la forme :
        FRED_API_KEY = xxx
    
    Avantage France vs Zone Euro :
    - Plusieurs séries sont disponibles en mensuel directement (pas besoin
        d'interpoler trimestriel : mensuel pour tous les indicateurs)
    - Couverture : production, emploi, inflation, commerce de détail,
        confiance consommateur, immatriculations automobiles (proxy demande)
        
"""

import os
import sys
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.style
import matplotlib.gridspec as gridspec

from matplotlib.figure import Figure
from matplotlib.patches import Patch
from matplotlib.ticker import FuncFormatter
from matplotlib import cm
import warnings
warnings.filterwarnings('ignore')

from datetime import datetime
from fredapi import Fred
from pathlib import Path
base = Path(__file__).resolve().parent.parent
sys.path.append( str(base) )

from styles.watermark import Watermark
from config.path import BASE_CONFIG_DIR
from dotenv import load_dotenv
load_dotenv( BASE_CONFIG_DIR / '.env' )

# ── Configuration ────────────────────────────────────────────────────────────

API_KEY      = os.getenv("FRED_API_KEY")
START        = '2020-01-01'
END          = datetime.now().strftime('%Y-%m-%d')
ROLL_ZSCORE  = 36   # fenêtre rolling Z-score (mois) — anti look-ahead
SMOOTH_SHORT =  3
SMOOTH_LONG  =  6

fred = Fred(api_key=API_KEY)

# ── Séries France disponibles sur FRED ───────────────────────────────────────
#
#  (ticker, label, pct_change_lag, inversion, poids)
#  inversion=True : une hausse est négative pour la macro (chômage, inflation)

#from macro_indices.allemagne import INDEX_CONFIG
from macro_indices.france import INDEX_CONFIG

INDEX_NAME = INDEX_CONFIG['name']
SERIES_CONFIG = INDEX_CONFIG['series']

# Normalisation des poids (somme = 1)
total_weight = sum(r[4] for r in SERIES_CONFIG)
SERIES_CONFIG = [(t, l, lag, inv, w / total_weight) for t, l, lag, inv, w in SERIES_CONFIG]

# ── Téléchargement ────────────────────────────────────────────────────────────

print(f"[{END}] Téléchargement des séries FRED — {INDEX_NAME}")
raw = {}
for ticker, label, *_ in SERIES_CONFIG:
    _msg = f"  • {ticker}  ({label}) last date: "
    raw[ticker] = fred.get_series( ticker, observation_start=START, observation_end=END )
    _msg = _msg + str( raw[ticker].index[-1] )
    print( _msg )
    
# ── Harmonisation mensuelle ───────────────────────────────────────────────────
# Certaines séries peuvent être trimestrielles ; interpolation linéaire en mensuel.

df_raw = {}
for ticker, series in raw.items():
    df_raw[ticker] = (
        series
        .resample('MS').interpolate('time')
        .interpolate('time')
    )
    
df_raw = pd.DataFrame(df_raw)
df_raw = df_raw.loc[START:]

# ── Transformations ───────────────────────────────────────────────────────────

df = pd.DataFrame(index=df_raw.index)

for ticker, label, lag, inv, _ in SERIES_CONFIG:
    s = df_raw[ticker]
    transformed = s.pct_change(lag) if lag > 1 else s.diff(1)
    df[f'T_{ticker}'] = -transformed if inv else transformed

# ── Z-score ROLLING (anti look-ahead) ────────────────────────────────────────

for ticker, *_ in SERIES_CONFIG:
    col = f'T_{ticker}'
    roll = df[col].rolling(ROLL_ZSCORE, min_periods=12)
    df[f'Z_{ticker}'] = (df[col] - roll.mean()) / roll.std()

# ── Score composite pondéré ───────────────────────────────────────────────────

df['MACRO_ISM'] = sum(
    w * df[f'Z_{ticker}']
    for ticker, _, _lag, _inv, w in SERIES_CONFIG
)

df['MACRO_ISM_S']  = df['MACRO_ISM'].rolling(SMOOTH_LONG).mean()
df['MACRO_ISM_MO'] = df['MACRO_ISM'].rolling(SMOOTH_SHORT).mean()
df['MOMENTUM']     = df['MACRO_ISM_S'].diff(1)

# ── Probabilité de récession (sigmoïde inversée) ──────────────────────────────

df['RECESSION_PROB'] = 1 / (1 + np.exp(2 * df['MACRO_ISM_S']))

# ── Régimes (5 états) ────────────────────────────────────────────────────────

REGIME_COLORS = {
    'forte_expansion':   ('#1a7a2e', '#c8f5d4'),
    'expansion':         ('#4caf50', '#e8f5e9'),
    'neutre':            ('#90a4ae', '#f0f4f8'),
    'contraction':       ('#ef9a9a', '#fff3f3'),
    'forte_contraction': ('#c62828', '#ffebee'),
}

def classify(score):
    if   score >  1.0: return 'forte_expansion'
    elif score >  0.4: return 'expansion'
    elif score > -0.4: return 'neutre'
    elif score > -1.0: return 'contraction'
    else:              return 'forte_contraction'

df['REGIME'] = df['MACRO_ISM_S'].apply(classify)
df = df.dropna(subset=['MACRO_ISM_S'])

# ── Export CSV ────────────────────────────────────────────────────────────────

# export_cols = (
#     ['MACRO_ISM', 'MACRO_ISM_S', 'MACRO_ISM_MO', 'MOMENTUM', 'RECESSION_PROB', 'REGIME']
#     + [f'Z_{t}' for t, *_ in SERIES_CONFIG]
# )
# df[export_cols].to_csv('macro_ism_france.csv')
# print("Export CSV → macro_ism_france.csv")

# ── Résumé terminal ───────────────────────────────────────────────────────────

last = df.iloc[-1]
print(f"""
╔══════════════════════════════════════════════════╗
║         MACRO ISM {INDEX_NAME} — DERNIER POINT        ║
╠══════════════════════════════════════════════════╣
║  Date            : {df.index[-1].strftime('%Y-%m')}
║  Score brut      : {last['MACRO_ISM']:+.3f}
║  Score lissé     : {last['MACRO_ISM_S']:+.3f}
║  Momentum        : {last['MOMENTUM']:+.3f}
║  Proba récession : {last['RECESSION_PROB']:.1%}
║  Régime          : {last['REGIME'].upper()}
╚══════════════════════════════════════════════════╝
""")

# ── Graphique multi-panneaux ──────────────────────────────────────────────────

def plot_graphs():
    matplotlib.style.use("seaborn-v0_8-notebook")
    
    fig = Figure( figsize=(14, 10) )
    Watermark.apply( fig )

    gs  = gridspec.GridSpec(4, 1, figure=fig, hspace=0.45, wspace=0.30)

    ax_main = fig.add_subplot(gs[0, :])
    ax_mom  = fig.add_subplot(gs[1, :])
    ax_rec  = fig.add_subplot(gs[2, :])
    ax_comp = fig.add_subplot(gs[3, :])

    # Panneau 1 — Score principal ─────────────────────────────────────────────────

    for i in range(1, len(df)):
        regime = df['REGIME'].iloc[i]
        _, bg = REGIME_COLORS[regime]
        ax_main.axvspan(df.index[i-1], df.index[i], color=bg, alpha=0.9, linewidth=0)

    ax_main.plot(df.index, df['MACRO_ISM'],   color='#9e9e9e', lw=0.8, alpha=0.6, label='Brut')
    ax_main.plot(df.index, df['MACRO_ISM_S'], color='#00267F', lw=2.2, label=f'Lissé ({SMOOTH_LONG}m)')
    ax_main.axhline( 1.0, ls=':', color='#2e7d32', lw=1)
    ax_main.axhline( 0.4, ls='--', color='#4caf50', lw=0.8)
    ax_main.axhline( 0,   ls='-',  color='black',   lw=1)
    ax_main.axhline(-0.4, ls='--', color='#ef9a9a', lw=0.8)
    ax_main.axhline(-1.0, ls=':', color='#c62828', lw=1)

    legend_patches = [Patch(facecolor=bg, label=r.replace('_', ' ').title()) for r, (_, bg) in REGIME_COLORS.items()]
    ax_main.legend(handles=legend_patches + ax_main.get_lines()[:2],
                loc='lower left', fontsize=8, ncol=4)
    ax_main.set_title( f"Macro ISM {INDEX_NAME} — Indice composite pondéré (FRED / OCDE)", fontsize=13, fontweight='bold')
    ax_main.set_ylabel('Z-score pondéré')

    # Panneau 2 — Momentum ────────────────────────────────────────────────────────

    colors_mom = ['#c62828' if v < 0 else '#2e7d32' for v in df['MOMENTUM']]
    ax_mom.bar(df.index, df['MOMENTUM'], color=colors_mom, width=20, alpha=0.8)
    ax_mom.axhline(0, color='black', lw=0.8)
    ax_mom.set_title('Momentum (Δ score lissé 1m)', fontsize=10)
    ax_mom.set_ylabel('Δ score')

    # Panneau 3 — Probabilité de récession ───────────────────────────────────────

    ax_rec.fill_between(df.index, df['RECESSION_PROB'], alpha=0.7, color='#EF4135')
    ax_rec.axhline(0.5, ls='--', color='black', lw=1)
    ax_rec.set_ylim(0, 1)
    ax_rec.set_title( f"Probabilité de récession {INDEX_NAME} (sigmoïde)", fontsize=10 )
    ax_rec.set_ylabel('Probabilité')
    ax_rec.yaxis.set_major_formatter(FuncFormatter(lambda x, _: f'{x:.0%}'))

    # Panneau 4 — Contributions par composante ────────────────────────────────────

    comp_cols  = [f'Z_{t}' for t, *_ in SERIES_CONFIG]
    labels     = [l for _, l, *_ in SERIES_CONFIG]
    weights    = [w for *_, w in SERIES_CONFIG]

    LAST_MONTHS = 50
    recent   = df[comp_cols].tail(LAST_MONTHS)
    weighted = recent.multiply(weights)

    bottom_pos = pd.Series(0.0, index=recent.index)
    bottom_neg = pd.Series(0.0, index=recent.index)
    colors_comp = cm.tab10(np.linspace(0, 1, len(comp_cols)))

    for col, label, color in zip(weighted.columns, labels, colors_comp):
        vals = weighted[col]
        pos  = vals.clip(lower=0)
        neg  = vals.clip(upper=0)
        ax_comp.bar(recent.index, pos, bottom=bottom_pos, width=20,
                    label=label, color=color, alpha=0.85)
        ax_comp.bar(recent.index, neg, bottom=bottom_neg, width=20,
                    color=color, alpha=0.85)
        bottom_pos = bottom_pos + pos
        bottom_neg = bottom_neg + neg

    ax_comp.axhline(0, color='black', lw=0.8)
    ax_comp.set_title(f'Contributions pondérées par composante ({LAST_MONTHS} derniers mois)', fontsize=10)
    ax_comp.set_ylabel('Contribution au score')
    ax_comp.legend(loc='upper left', fontsize=7, ncol=3)

    fig.suptitle( f'Macro ISM {INDEX_NAME} | Données FRED / OCDE | Généré le {END}', fontsize=11, style='italic', color='#555')
    fig.tight_layout()
    
    return fig

def main():
    matplotlib.use("Agg")
    from user_scripts.api import api
    
    fig = plot_graphs()
    api.show_figure( fig, title=f"{api.ticker} - Bull/Bear Strength Index" )

if __name__ == "__main__":
    matplotlib.use("TkAgg")

    fig = plot_graphs()
    fig.tight_layout()

    # Créer la fenêtre Tkinter
    import tkinter as tk
    from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk

    root = tk.Tk()
    root.title(f"Bull/Bear Strength Index")
    canvas = FigureCanvasTkAgg(fig, master=root)
    toolbar = NavigationToolbar2Tk(canvas, root)
    toolbar.update()
    
    canvas.draw()
    canvas.get_tk_widget().pack(fill="both", expand=True)
    root.mainloop()