megaproxy
61a7884ee5
Full-stack self-hosted finance app with FastAPI backend and React frontend. Features: - Accounts, transactions, budgets, investments with GBP base currency - CSV import with auto-detection for 10 UK bank formats - ML predictions: spending forecast, net worth projection, Monte Carlo - 7 selectable themes (Obsidian, Arctic, Midnight, Vault, Terminal, Synthwave, Ledger) - Receipt/document attachments on transactions (JPEG, PNG, WebP, PDF) - AES-256-GCM field encryption, RS256 JWT, TOTP 2FA, RLS, audit log - Encrypted nightly backups + key rotation script - Mobile-responsive layout with bottom nav Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
from __future__ import annotations
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import warnings
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from datetime import date
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from dateutil.relativedelta import relativedelta
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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MIN_POINTS = 3
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FORECAST_MONTHS = 3
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def _next_month_starts(from_date: date, n: int) -> list[str]:
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months = []
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d = (from_date.replace(day=1) + relativedelta(months=1))
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for _ in range(n):
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months.append(d.strftime("%Y-%m-%d"))
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d += relativedelta(months=1)
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return months
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def _fit_holt(values: list[float], n: int) -> tuple[list[float], list[float], list[float]]:
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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try:
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if len(values) >= 12:
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model = ExponentialSmoothing(values, trend="add", seasonal="add", seasonal_periods=12)
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elif len(values) >= 4:
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model = ExponentialSmoothing(values, trend="add", seasonal=None)
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else:
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model = ExponentialSmoothing(values, trend=None, seasonal=None)
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fit = model.fit(optimized=True, disp=False)
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forecast = fit.forecast(n)
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sigma = float(np.std(fit.resid)) if len(fit.resid) > 1 else float(np.mean(values) * 0.15)
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lower = np.maximum(0, forecast - 1.28 * sigma)
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upper = forecast + 1.28 * sigma
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return forecast.tolist(), lower.tolist(), upper.tolist()
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except Exception:
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avg = float(np.mean(values))
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sigma = float(np.std(values)) if len(values) > 1 else avg * 0.15
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return [avg] * n, [max(0, avg - 1.28 * sigma)] * n, [(avg + 1.28 * sigma)] * n
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def forecast_spending(df: pd.DataFrame) -> list[dict]:
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"""
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df columns: category_id, category_name, ds (monthly), y (amount)
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Returns list of category forecast dicts.
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"""
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if df.empty:
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return []
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today = date.today()
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future_dates = _next_month_starts(today, FORECAST_MONTHS)
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results = []
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for (cat_id, cat_name), group in df.groupby(["category_id", "category_name"]):
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group = group.sort_values("ds")
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values = group["y"].tolist()
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actuals = [
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{"date": row["ds"].strftime("%Y-%m-%d"), "amount": row["y"]}
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for _, row in group.iterrows()
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]
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if len(values) < MIN_POINTS:
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avg = float(np.mean(values))
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forecast_pts = [
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{"date": d, "amount": round(avg, 2), "lower": round(avg * 0.7, 2), "upper": round(avg * 1.3, 2)}
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for d in future_dates
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]
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else:
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fcast, lower, upper = _fit_holt(values, FORECAST_MONTHS)
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forecast_pts = [
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{"date": d, "amount": round(max(0, f), 2), "lower": round(l, 2), "upper": round(u, 2)}
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for d, f, l, u in zip(future_dates, fcast, lower, upper)
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]
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results.append({
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"category_id": cat_id,
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"category_name": cat_name,
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"monthly_avg": round(float(np.mean(values)), 2),
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"actuals": actuals[-6:], # last 6 months for display
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"forecast": forecast_pts,
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})
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# Sort by monthly_avg descending (highest spend first)
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results.sort(key=lambda x: x["monthly_avg"], reverse=True)
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return results
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