megaproxy
a7e58d6aa1
asyncpg infers :days as an integer, causing a 'date >= integer' type error in PostgreSQL. Compute the cutoff date in Python and bind it as a date parameter instead. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
122 lines
4.7 KiB
Python
122 lines
4.7 KiB
Python
from __future__ import annotations
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from datetime import date, timedelta
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import pandas as pd
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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async def get_monthly_category_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
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result = await db.execute(text("""
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SELECT
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COALESCE(t.category_id::text, 'uncategorised') AS category_id,
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COALESCE(c.name, 'Uncategorised') AS category_name,
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DATE_TRUNC('month', t.date)::date AS ds,
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SUM(ABS(t.amount))::float AS y
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FROM transactions t
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LEFT JOIN categories c ON c.id = t.category_id
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WHERE t.user_id = CAST(:uid AS uuid)
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AND t.type = 'expense'
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AND t.deleted_at IS NULL
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AND t.status != 'void'
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GROUP BY t.category_id, c.name, DATE_TRUNC('month', t.date)
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ORDER BY ds ASC
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"""), {"uid": str(user_id)})
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rows = result.fetchall()
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if not rows:
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return pd.DataFrame(columns=["category_id", "category_name", "ds", "y"])
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df = pd.DataFrame(rows, columns=["category_id", "category_name", "ds", "y"])
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df["ds"] = pd.to_datetime(df["ds"])
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df["y"] = df["y"].astype(float)
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return df
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async def get_monthly_net_worth(db: AsyncSession, user_id: str) -> pd.DataFrame:
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result = await db.execute(text("""
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SELECT date::text AS ds, net_worth::float AS y
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FROM net_worth_snapshots
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WHERE user_id = CAST(:uid AS uuid)
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ORDER BY date ASC
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"""), {"uid": str(user_id)})
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rows = result.fetchall()
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if not rows:
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return pd.DataFrame(columns=["ds", "y"])
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df = pd.DataFrame(rows, columns=["ds", "y"])
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df["ds"] = pd.to_datetime(df["ds"])
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df["y"] = df["y"].astype(float)
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# Resample to monthly end, keeping last value
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df = df.set_index("ds").resample("ME").last().dropna().reset_index()
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df.columns = ["ds", "y"]
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return df
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async def get_current_month_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
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result = await db.execute(text("""
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SELECT
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COALESCE(t.category_id::text, 'uncategorised') AS category_id,
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COALESCE(c.name, 'Uncategorised') AS category_name,
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SUM(ABS(t.amount))::float AS spent
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FROM transactions t
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LEFT JOIN categories c ON c.id = t.category_id
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WHERE t.user_id = CAST(:uid AS uuid)
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AND t.type = 'expense'
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AND t.deleted_at IS NULL
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AND t.status != 'void'
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AND DATE_TRUNC('month', t.date) = DATE_TRUNC('month', CURRENT_DATE)
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GROUP BY t.category_id, c.name
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"""), {"uid": str(user_id)})
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rows = result.fetchall()
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if not rows:
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return pd.DataFrame(columns=["category_id", "category_name", "spent"])
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df = pd.DataFrame(rows, columns=["category_id", "category_name", "spent"])
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df["spent"] = df["spent"].astype(float)
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return df
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async def get_portfolio_monthly_returns(db: AsyncSession, user_id: str) -> pd.DataFrame:
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"""Monthly close prices for each asset in user's portfolio."""
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result = await db.execute(text("""
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SELECT
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a.symbol,
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DATE_TRUNC('month', ap.date)::date AS month,
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(ARRAY_AGG(ap.close ORDER BY ap.date DESC))[1]::float AS close
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FROM investment_holdings h
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JOIN assets a ON a.id = h.asset_id
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JOIN asset_prices ap ON ap.asset_id = h.asset_id
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WHERE h.user_id = CAST(:uid AS uuid)
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AND h.deleted_at IS NULL
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GROUP BY a.symbol, DATE_TRUNC('month', ap.date)
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ORDER BY a.symbol, month ASC
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"""), {"uid": str(user_id)})
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rows = result.fetchall()
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if not rows:
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return pd.DataFrame(columns=["symbol", "month", "close"])
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df = pd.DataFrame(rows, columns=["symbol", "month", "close"])
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df["month"] = pd.to_datetime(df["month"])
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df["close"] = df["close"].astype(float)
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return df
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async def get_daily_cash_flow(db: AsyncSession, user_id: str, days: int = 90) -> pd.DataFrame:
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cutoff = date.today() - timedelta(days=days)
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result = await db.execute(text("""
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SELECT
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t.date::date AS ds,
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SUM(CASE WHEN t.amount > 0 THEN t.amount ELSE 0 END)::float AS inflow,
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SUM(CASE WHEN t.amount < 0 THEN ABS(t.amount) ELSE 0 END)::float AS outflow
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FROM transactions t
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WHERE t.user_id = CAST(:uid AS uuid)
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AND t.deleted_at IS NULL
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AND t.status != 'void'
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AND t.type IN ('income', 'expense')
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AND t.date >= :cutoff
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GROUP BY t.date
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ORDER BY t.date ASC
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"""), {"uid": str(user_id), "cutoff": cutoff})
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rows = result.fetchall()
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if not rows:
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return pd.DataFrame(columns=["ds", "inflow", "outflow"])
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df = pd.DataFrame(rows, columns=["ds", "inflow", "outflow"])
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df["ds"] = pd.to_datetime(df["ds"])
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return df
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