eb0dd3ddbf
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refresh on scenario edits Two fixes for the user's report that adding a £100k life-event spend didn't change the chart: Engine (simulator.py) - New `extra_outflows` param. cashflow_adjustments still drains the portfolio at start-of-year as before, but the simulator now ALSO records the spending in `withdrawal_hist[p, y]` so the chart's red median-withdrawal trace shows the bump. Without this, the £100k silently came out of the portfolio but the user-facing withdrawal trace stayed at the strategy's flat 4% draw. - simulate.py wires extra_outflows = essential + discretionary category outflows from life events. UX (ScenarioDetail.tsx) - New auto-refresh: when life events / income streams / flex rules change for a scenario, the page fires `/simulate` automatically with 2,000 paths and uses the result as the primary fan/year-stats source. The persisted MC run is only consulted as a fallback for scenarios with no overrides. - Fan chart title gains a "live preview · Xs · Ny" pill while a sim is current, and "re-running…" while a fresh one is in flight. - Removed the now-redundant "Live preview run" duplicate card lower down — the main chart IS the live preview. - Year-stats badge row reads from sim.data when available so changes propagate immediately to NW / Δ NW / Spending / Taxes. 247 pytest pass (+1 new); mypy + ruff clean; frontend typecheck/test/ build green.
272 lines
11 KiB
Python
272 lines
11 KiB
Python
"""Sync simulate + multi-scenario compare.
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Unlike the persisted Cartesian recompute (`/recompute`), these run a
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single scenario inline and return the result immediately. The React UI
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uses these for what-if exploration — no DB write.
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Returns a fan-chart series in the same shape as
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`GET /scenarios/{id}/projection`, so frontend chart code is shared.
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"""
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from __future__ import annotations
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import asyncio
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import time
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from decimal import Decimal
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from pathlib import Path
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import numpy as np
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from fastapi import APIRouter, HTTPException
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from sqlalchemy.ext.asyncio import async_sessionmaker
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from fire_planner.api.schemas import (
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CompareRequest,
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CompareResult,
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GoalProbability,
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ProjectionPoint,
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SimulateRequest,
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SimulateResult,
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)
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from fire_planner.flex_spending import FlexRule as EngineFlexRule
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from fire_planner.glide_path import static
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from fire_planner.goals_eval import evaluate_goals
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from fire_planner.income_streams import IncomeStreamInput, streams_to_arrays
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from fire_planner.ingest.wealthfolio_pg import create_wf_sync_engine_from_env
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from fire_planner.life_events import (
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EventInput,
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events_to_cashflow_array,
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events_to_category_outflows,
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)
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from fire_planner.returns.bootstrap import block_bootstrap
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from fire_planner.returns.shiller import load_from_csv, synthetic_returns
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from fire_planner.returns.wealthfolio_returns import (
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compute_annual_returns_from_pg,
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constant_real_return_paths,
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)
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from fire_planner.scenarios import build_regime_schedule, build_strategy
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from fire_planner.simulator import SimulationResult, build_fixed_paths, simulate
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router = APIRouter(tags=["simulate"])
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_RETURNS_CSV = Path("/data/shiller_returns.csv")
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def _shiller_paths(seed: int, n_paths: int, n_years: int) -> np.ndarray:
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bundle = (load_from_csv(_RETURNS_CSV) if _RETURNS_CSV.exists() else synthetic_returns(seed=42))
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rng = np.random.default_rng(seed)
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return block_bootstrap(bundle, n_paths=n_paths, n_years=n_years, block_size=5, rng=rng)
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async def _wealthfolio_paths(seed: int, n_paths: int, n_years: int) -> np.ndarray:
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"""Block-bootstrap the user's actual blended real returns. With
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typically <10 distinct annual samples, block_size=1 is appropriate
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— there's no serial-correlation signal to preserve."""
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eng = create_wf_sync_engine_from_env()
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try:
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factory = async_sessionmaker(eng, expire_on_commit=False)
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async with factory() as wf_sess:
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bundle = await compute_annual_returns_from_pg(wf_sess)
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finally:
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await eng.dispose()
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rng = np.random.default_rng(seed)
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return block_bootstrap(bundle, n_paths=n_paths, n_years=n_years, block_size=1, rng=rng)
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async def _build_paths(req: SimulateRequest) -> np.ndarray:
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if req.rates_mode == "fixed":
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return build_fixed_paths(
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n_paths=req.n_paths,
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n_years=req.horizon_years,
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inflation_pct=float(req.inflation_pct),
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stocks_growth_pct=float(req.stocks_growth_pct),
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stocks_dividend_pct=float(req.stocks_dividend_pct),
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bonds_growth_pct=float(req.bonds_growth_pct),
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bonds_dividend_pct=float(req.bonds_dividend_pct),
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)
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if req.returns_mode == "manual":
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if req.manual_real_return_pct is None:
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raise HTTPException(
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status_code=400,
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detail="manual_real_return_pct is required when returns_mode='manual'",
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)
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return constant_real_return_paths(
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n_paths=req.n_paths,
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n_years=req.horizon_years,
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real_return_pct=float(req.manual_real_return_pct),
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)
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if req.returns_mode == "wealthfolio":
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try:
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return await _wealthfolio_paths(req.seed, req.n_paths, req.horizon_years)
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except ValueError as e:
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raise HTTPException(
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status_code=400,
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detail=f"Wealthfolio history insufficient: {e}",
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) from e
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return _shiller_paths(req.seed, req.n_paths, req.horizon_years)
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def _project(req: SimulateRequest, paths: np.ndarray) -> tuple[SimulationResult, float]:
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annual_savings = (np.full(req.horizon_years, float(req.savings_per_year_gbp), dtype=np.float64)
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if req.savings_per_year_gbp > 0 else None)
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floor = float(req.floor_gbp) if req.floor_gbp is not None else None
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cashflow_adjustments = None
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discretionary_outflows = None
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extra_outflows = None
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if req.life_events:
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engine_events = [
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EventInput(
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year_start=ev.year_start,
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year_end=ev.year_end,
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delta_gbp_per_year=float(ev.delta_gbp_per_year),
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one_time_amount_gbp=(float(ev.one_time_amount_gbp)
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if ev.one_time_amount_gbp is not None else None),
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category=ev.category,
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enabled=ev.enabled,
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) for ev in req.life_events
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]
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cashflow_adjustments = events_to_cashflow_array(engine_events, req.horizon_years)
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category_outflows = events_to_category_outflows(engine_events, req.horizon_years)
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discretionary_outflows = category_outflows.get("discretionary")
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# extra_outflows feeds the withdrawal-trace display: total of
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# essential + discretionary spending events surfaces alongside
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# the strategy's draw on the chart.
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essential = category_outflows.get("essential")
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if essential is not None and discretionary_outflows is not None:
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extra_outflows = essential + discretionary_outflows
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engine_flex = [
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EngineFlexRule(
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from_ath_pct=float(r.from_ath_pct),
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cut_discretionary_pct=float(r.cut_discretionary_pct),
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) for r in req.flex_rules
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] if req.flex_rules else None
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income_inflows = None
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income_taxable = None
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if req.income_streams:
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engine_streams = [
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IncomeStreamInput(
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kind=s.kind,
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start_year=s.start_year,
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end_year=s.end_year,
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amount_gbp_per_year=float(s.amount_gbp_per_year),
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growth_pct=float(s.growth_pct),
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tax_treatment=s.tax_treatment,
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enabled=s.enabled,
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) for s in req.income_streams
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]
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income_inflows, income_taxable = streams_to_arrays(engine_streams, req.horizon_years)
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strategy = build_strategy(
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req.strategy,
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floor=floor,
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annual_real_adjust_pct=float(req.annual_real_adjust_pct),
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guardrail_threshold_pct=(float(req.guardrail_threshold_pct)
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if req.guardrail_threshold_pct is not None else None),
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guardrail_cut_pct=float(req.guardrail_cut_pct),
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)
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glide_alloc = float(req.stocks_allocation) if req.rates_mode == "fixed" else 1.0
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started = time.perf_counter()
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result = simulate(
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paths=paths,
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initial_portfolio=float(req.nw_seed_gbp),
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spending_target=float(req.spending_gbp),
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glide=static(glide_alloc),
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strategy=strategy,
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regime=build_regime_schedule(req.jurisdiction, req.leave_uk_year),
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horizon_years=req.horizon_years,
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annual_savings=annual_savings,
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cashflow_adjustments=cashflow_adjustments,
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income_inflows=income_inflows,
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income_taxable=income_taxable,
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discretionary_outflows=discretionary_outflows,
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extra_outflows=extra_outflows,
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flex_rules=engine_flex,
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)
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elapsed = time.perf_counter() - started
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return result, elapsed
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def _to_response(
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result: SimulationResult,
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elapsed: float,
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req: SimulateRequest | None = None,
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) -> SimulateResult:
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# portfolio_real has n_years+1 columns (year 0 = seed, year k = end-of-year k).
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# withdrawal_real / tax_real have n_years columns (year k = withdrawn in year k+1).
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# Yearly point k describes "end of year k+1": portfolio after withdrawal & growth.
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pcts = [10, 25, 50, 75, 90]
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portfolio_quantiles = {p: np.percentile(result.portfolio_real, p, axis=0) for p in pcts}
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median_wd = np.percentile(result.withdrawal_real, 50, axis=0)
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median_tax = np.percentile(result.tax_real, 50, axis=0)
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n_years = result.n_years
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survival_path = (result.success_mask.astype(np.float64).mean(axis=0) if
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result.success_mask.ndim == 2 else np.ones(n_years))
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yearly = [
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ProjectionPoint(
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year_idx=y,
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p10_portfolio_gbp=Decimal(str(round(float(portfolio_quantiles[10][y + 1]), 2))),
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p25_portfolio_gbp=Decimal(str(round(float(portfolio_quantiles[25][y + 1]), 2))),
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p50_portfolio_gbp=Decimal(str(round(float(portfolio_quantiles[50][y + 1]), 2))),
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p75_portfolio_gbp=Decimal(str(round(float(portfolio_quantiles[75][y + 1]), 2))),
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p90_portfolio_gbp=Decimal(str(round(float(portfolio_quantiles[90][y + 1]), 2))),
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p50_withdrawal_gbp=Decimal(str(round(float(median_wd[y]), 2))),
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p50_tax_gbp=Decimal(str(round(float(median_tax[y]), 2))),
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survival_rate=Decimal(str(round(float(survival_path[y]), 4))),
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) for y in range(n_years)
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]
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median_ytr = result.median_years_to_ruin()
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goals_probability: list[GoalProbability] = []
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if req is not None and req.goals:
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evaluations = evaluate_goals(result, req.goals, req.horizon_years)
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goals_probability = [
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GoalProbability(
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goal_id=None,
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name=ev.name,
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kind=ev.kind,
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probability=Decimal(str(round(ev.probability, 4))),
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threshold=Decimal(str(round(ev.threshold, 4))),
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passed=ev.passed,
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) for ev in evaluations
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]
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return SimulateResult(
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success_rate=Decimal(str(round(float(result.success_rate), 4))),
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p10_ending_gbp=Decimal(str(round(float(result.ending_percentile(10)), 2))),
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p50_ending_gbp=Decimal(str(round(float(result.ending_percentile(50)), 2))),
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p90_ending_gbp=Decimal(str(round(float(result.ending_percentile(90)), 2))),
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median_lifetime_tax_gbp=Decimal(str(round(float(result.median_lifetime_tax()), 2))),
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median_years_to_ruin=(Decimal(str(round(float(median_ytr), 2)))
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if median_ytr is not None else None),
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elapsed_seconds=Decimal(str(round(elapsed, 3))),
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yearly=yearly,
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goals_probability=goals_probability,
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)
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@router.post("/simulate", response_model=SimulateResult)
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async def simulate_one(req: SimulateRequest) -> SimulateResult:
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"""Run one scenario synchronously, no DB write. ~1-3s for 5k paths."""
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paths = await _build_paths(req)
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try:
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result, elapsed = await asyncio.to_thread(_project, req, paths)
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except KeyError as e:
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raise HTTPException(status_code=400, detail=f"Unknown name: {e}") from None
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return _to_response(result, elapsed, req)
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@router.post("/compare", response_model=CompareResult)
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async def compare_scenarios(req: CompareRequest) -> CompareResult:
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"""Run 2-5 scenarios in parallel, return all results."""
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async def one(s: SimulateRequest) -> SimulateResult:
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paths = await _build_paths(s)
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result, elapsed = await asyncio.to_thread(_project, s, paths)
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return _to_response(result, elapsed, s)
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try:
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results = await asyncio.gather(*(one(s) for s in req.scenarios))
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except KeyError as e:
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raise HTTPException(status_code=400, detail=f"Unknown name: {e}") from None
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return CompareResult(results=results)
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