68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
"""
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database.py — Accès aux données historiques pour l'analyse technique.
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"""
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from sqlalchemy import text
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from app import db
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def get_historique_df(isin):
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"""Récupère l'historique complet d'un ISIN trié par date croissante.
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Retourne un DataFrame pandas ou None si aucune donnée.
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Colonnes : date, price, open, hight, low, vol, change
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"""
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import pandas as pd
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rows = db.session.execute(
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text(
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"SELECT date, price, `open`, hight, low, vol, `change` "
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"FROM historique WHERE isin = :isin ORDER BY date ASC"
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),
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{"isin": isin},
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).fetchall()
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if not rows:
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return None
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df = pd.DataFrame(rows, columns=["date", "price", "open", "hight", "low", "vol", "change"])
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# Conversion des types
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df["date"] = pd.to_datetime(df["date"])
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df["price"] = pd.to_numeric(df["price"], errors="coerce")
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df["open"] = pd.to_numeric(df["open"], errors="coerce")
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df["hight"] = pd.to_numeric(df["hight"], errors="coerce")
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df["low"] = pd.to_numeric(df["low"], errors="coerce")
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df["vol"] = pd.to_numeric(df["vol"], errors="coerce").astype("Int64")
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df["change"] = pd.to_numeric(df["change"], errors="coerce")
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# Nettoyage : doublons de dates (on garde la dernière)
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df = df.drop_duplicates(subset="date", keep="last")
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# Suppression des lignes sans prix (essentielles)
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df = df.dropna(subset=["price"])
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# Tri par date
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df = df.sort_values("date").reset_index(drop=True)
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return df
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def get_action_info(isin):
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"""Récupère les infos de l'action (company_name, ticker, currency).
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Retourne un dict ou None si l'ISIN n'existe pas dans le référentiel.
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"""
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row = db.session.execute(
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text("SELECT isin, ticker, company_name, currency FROM actions WHERE isin = :isin"),
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{"isin": isin},
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).fetchone()
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if not row:
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return None
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return {
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"isin": row.isin,
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"ticker": row.ticker or "",
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"company_name": row.company_name or "",
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"currency": row.currency or "EUR",
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}
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