feat: risk
This commit is contained in:
OpenSquared
@@ -1,70 +1,89 @@
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"""
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Portfolio scenario-exposure — answers "if macro scenario X happens, how does my ACTUAL
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book of open positions react, and how many of my positions are really the same bet wearing
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different tickers?" Distinct from two pre-existing, coarser tools:
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- services.data_fetcher.score_macro_scenarios(): the GLOBAL 8-scenario macro regime,
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qualitative asset-class bias only (bullish/bearish/neutral), used for the top-level
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regime badge — not calibrated to numeric spot shocks and has no gold-bearish or
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forex-directional case, so it can't tell two option positions apart.
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Portfolio scenario-exposure — answers "which of our 8 macro scenarios is my ACTUAL book of
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open positions really a bet on, and how many differently-named positions are secretly the
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same bet?" Deliberately reuses the SAME 8 scenarios as the global macro regime
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(services.data_fetcher.SCENARIO_META / SCENARIO_ASSET_BIAS — goldilocks, desinflation,
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soft_landing, reflation, stagflation, inflation_shock, recession, crise_liquidite) as its
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single source of truth for labels/colors/emoji and directional bias, so this tool can never
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show a scenario name or direction the rest of the app doesn't already agree with.
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Distinct from two other pre-existing, coarser tools:
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- services.data_fetcher.score_macro_scenarios(): scores which of the 8 scenarios the
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CURRENT macro gauges look like (top-down, market-wide) — this module instead scores
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which of the 8 scenarios OUR OWN POSITIONS are betting on (bottom-up, portfolio-wide).
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Same 8 buckets, opposite direction of inference.
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- services.database.get_risk_dashboard()/get_risk_clusters(): buckets capital by
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asset_class and by a geopolitical-trigger keyword match — blind to whether a position
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is long or short its underlying, so a bullish and a bearish position on the same
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ticker land in the same bucket.
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This module instead reprices each position's REAL legs (same Saxo-first pricing as
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services.portfolio_pricing, used by mark-to-market and the payoff chart) under a small set
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of named spot/vol shocks, so positions on different tickers that both profit from the same
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shock get flagged as the SAME risk bet — e.g. a short S&P call spread, a long gold put
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spread and a short crude call spread can all really be "one Risk-Off bet, three times."
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ticker land in the same bucket. Kept as a separate, complementary lens (thematic
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capital exposure) rather than merged with this one (directional scenario alignment).
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The evaluation itself is deliberately NOT an LLM call: it reprices each position's REAL legs
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(same Saxo-first pricing as services.portfolio_pricing, used by mark-to-market and the
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payoff chart) under each scenario's spot/vol shock via Black-Scholes. Same positions in,
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same percentages out, every time — auditable and tied to real strikes/greeks, matching how
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Curve Regime and the payoff diagram already work elsewhere in this app.
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"""
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from typing import Any, Dict, List, Optional, Tuple
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from services.data_fetcher import SCENARIO_META, SCENARIO_ASSET_BIAS
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SCENARIOS: List[Dict[str, str]] = [
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{"key": "risk_off", "label": "Risk-Off / Ralentissement"},
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{"key": "risk_on", "label": "Reprise économique / Risk-On"},
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{"key": "inflation_persistante", "label": "Inflation persistante"},
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{"key": "dollar_fort", "label": "Dollar fort"},
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{"key": "commodities_baisse", "label": "Baisse des matières premières"},
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{"key": key, "label": meta["label"], "color": meta["color"], "emoji": meta["emoji"]}
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for key, meta in SCENARIO_META.items()
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]
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# asset_class -> scenario_key -> (spot_shock_pct, vol_shock_abs added to the leg's resolved sigma)
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_DIMENSION_SHOCKS: Dict[str, Dict[str, Tuple[float, float]]] = {
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"indices": {
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"risk_off": (-0.08, 0.06), "risk_on": (0.07, -0.03), "inflation_persistante": (-0.05, 0.04),
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"dollar_fort": (-0.02, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"equities": {
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"risk_off": (-0.08, 0.06), "risk_on": (0.07, -0.03), "inflation_persistante": (-0.05, 0.04),
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"dollar_fort": (-0.02, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"energy": {
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"risk_off": (-0.10, 0.08), "risk_on": (0.08, -0.04), "inflation_persistante": (0.10, 0.05),
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"dollar_fort": (-0.05, 0.02), "commodities_baisse": (-0.12, 0.03),
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},
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"metals": {
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"risk_off": (0.05, 0.03), "risk_on": (-0.04, -0.02), "inflation_persistante": (0.08, 0.04),
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"dollar_fort": (-0.06, 0.02), "commodities_baisse": (-0.08, 0.02),
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},
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"agriculture": {
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"risk_off": (-0.03, 0.03), "risk_on": (0.03, -0.02), "inflation_persistante": (0.09, 0.04),
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"dollar_fort": (-0.04, 0.01), "commodities_baisse": (-0.10, 0.03),
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},
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"forex": {
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# Expressed as "USD strength" moves — sign is flipped per-pair by _fx_dollar_sign()
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# depending on whether USD is the base or quote currency.
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"risk_off": (0.03, 0.03), "risk_on": (-0.03, -0.02), "inflation_persistante": (-0.02, 0.03),
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"dollar_fort": (0.05, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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"rates": {
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"risk_off": (0.04, 0.02), "risk_on": (-0.03, -0.01), "inflation_persistante": (-0.06, 0.03),
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"dollar_fort": (0.01, 0.01), "commodities_baisse": (0.01, -0.01),
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},
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# Qualitative bias label (as used in SCENARIO_ASSET_BIAS) -> (spot_shock_pct, vol_shock_abs
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# added to the leg's resolved sigma). The only numeric calibration this module adds on top
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# of the existing qualitative macro-regime table.
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_BIAS_TO_SHOCK: Dict[str, Tuple[float, float]] = {
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"bullish+": (0.08, -0.03),
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"bullish": (0.04, -0.015),
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"neutral": (0.0, 0.0),
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"bearish": (-0.04, 0.015),
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"bearish+": (-0.08, 0.03),
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}
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# SCENARIO_ASSET_BIAS has no "rates" axis (bond ETFs like IEF/TLT) — extended here with the
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# same 8 scenario keys, standard macro logic: bonds rally when yields fall (recession,
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# desinflation, flight-to-quality in a liquidity crisis), sell off when inflation runs hot
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# (reflation, stagflation, inflation shock).
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_RATES_BIAS: Dict[str, str] = {
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"goldilocks": "neutral", "desinflation": "bullish+", "soft_landing": "bullish",
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"reflation": "bearish", "stagflation": "bearish+", "inflation_shock": "bearish+",
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"recession": "bullish+", "crise_liquidite": "bullish",
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}
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# SCENARIO_ASSET_BIAS's forex axis is only ever "neutral" or "defensive" (a flight-to-USD
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# flag, not a direction) — translated here into a dollar-strength shock. "defensive"
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# scenarios push capital into USD (dollar up); "neutral" scenarios leave it flat. The actual
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# sign applied to a given pair (EURUSD falls / USDJPY rises on dollar strength) is resolved
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# per-position by _fx_dollar_sign().
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_FOREX_DEFENSIVE_SHOCK: Tuple[float, float] = (0.04, 0.02)
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_DIRECT_ASSET_CLASSES = {"energy", "metals", "indices", "equities", "agriculture"}
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def _dimension_shock(asset_class: str, scenario_key: str) -> Tuple[float, float]:
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"""Spot/vol shock for one asset_class under one of the 8 canonical scenarios, derived
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from SCENARIO_ASSET_BIAS wherever that axis exists, with a documented extension for
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rates (not covered by the shared table) and forex (covered only as a non-directional
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flight-to-USD flag there)."""
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bias_row = SCENARIO_ASSET_BIAS[scenario_key]
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if asset_class in _DIRECT_ASSET_CLASSES:
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return _BIAS_TO_SHOCK.get(bias_row.get(asset_class, "neutral"), (0.0, 0.0))
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if asset_class == "rates":
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return _BIAS_TO_SHOCK.get(_RATES_BIAS.get(scenario_key, "neutral"), (0.0, 0.0))
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if asset_class == "forex":
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return _FOREX_DEFENSIVE_SHOCK if bias_row.get("forex") == "defensive" else (0.0, 0.0)
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# Unknown/uncategorized asset_class — fall back to the indices axis (equity-like default).
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return _BIAS_TO_SHOCK.get(bias_row.get("indices", "neutral"), (0.0, 0.0))
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def _fx_dollar_sign(ticker: str) -> int:
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"""+1 if USD is the base currency (pair rises when USD strengthens, e.g. USDJPY),
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-1 if USD is the quote currency (pair falls when USD strengthens, e.g. EURUSD),
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0 for a non-USD cross where the "dollar strength" dimension doesn't clearly apply."""
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0 for a non-USD cross where the dollar-strength dimension doesn't clearly apply."""
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t = (ticker or "").upper().replace("=X", "").replace("/", "")
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if t.startswith("USD"):
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return 1
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@@ -131,12 +150,14 @@ def _reprice_position(pos: Dict[str, Any], spot_shock_pct: float, vol_shock_abs:
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def compute_scenario_exposure() -> Dict[str, Any]:
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"""Reprices every open position under each named scenario, then aggregates two views:
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- `scenarios`: per-scenario portfolio-wide estimated P&L (the "sensitivity matrix").
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"""Reprices every open position under each of the 8 canonical macro scenarios, then
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aggregates two views:
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- `scenarios`: per-scenario portfolio-wide estimated P&L (the "sensitivity matrix"),
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sorted by |impact| so the scenarios that matter most float to the top.
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- `concentration`: for each position, the scenario that would benefit it MOST, then
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the capital-weighted % of the portfolio sharing that same dominant scenario (the
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"X% of your book is really one bet" bars) — the whole point being to surface when
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several differently-named positions are actually the same directional wager.
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the capital-weighted % of the portfolio sharing that same dominant scenario — the
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"our book is really one bet, repeated" ranking, using the exact same scenario
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names/colors as the global macro regime badge.
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"""
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from services.database import get_positions
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@@ -149,13 +170,12 @@ def compute_scenario_exposure() -> Dict[str, Any]:
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unpriced: List[Dict[str, Any]] = []
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for pos in positions:
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ac = (pos.get("asset_class") or "indices").lower()
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dims = _DIMENSION_SHOCKS.get(ac, _DIMENSION_SHOCKS["indices"])
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fx_sign = _fx_dollar_sign(pos["underlying"]) if ac == "forex" else 1
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scenario_pnl: Dict[str, Optional[float]] = {}
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for scen in SCENARIOS:
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key = scen["key"]
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spot_shock, vol_shock = dims.get(key, (0.0, 0.0))
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spot_shock, vol_shock = _dimension_shock(ac, key)
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if ac == "forex":
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spot_shock = spot_shock * fx_sign
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scenario_pnl[key] = _reprice_position(pos, spot_shock, vol_shock)
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@@ -182,7 +202,7 @@ def compute_scenario_exposure() -> Dict[str, Any]:
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key = scen["key"]
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total_pnl = sum(p["scenario_pnl"].get(key) or 0 for p in priced)
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scenario_results.append({
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"key": key, "label": scen["label"],
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"key": key, "label": scen["label"], "color": scen["color"], "emoji": scen["emoji"],
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"portfolio_pnl": round(total_pnl, 2),
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"portfolio_pnl_pct": round(total_pnl / total_capital * 100, 2),
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"positions": [
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@@ -203,7 +223,7 @@ def compute_scenario_exposure() -> Dict[str, Any]:
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weight_by_scenario[best_key] = weight_by_scenario.get(best_key, 0.0) + p["capital_invested"]
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concentration = [
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{"key": key, "label": next(s["label"] for s in SCENARIOS if s["key"] == key),
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{"key": key, **{k: v for k, v in next(s for s in SCENARIOS if s["key"] == key).items() if k != "key"},
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"pct_of_portfolio": round(w / total_capital * 100, 1)}
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for key, w in weight_by_scenario.items() if w > 0
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]
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@@ -827,8 +827,7 @@ export default function Dashboard() {
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.filter(d => d.value > 0)
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.sort((a, b) => b.value - a.value)
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const radarAxes = ((riskRadarData as any)?.axes ?? []).map((a: any) => ({ ...a, value: a.value ?? 0 }))
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const dominantScenario = (scenarioExposure as any)?.dominant_scenario
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const scenarioWarning = (scenarioExposure as any)?.warning
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const scenarioRanking: any[] = ((scenarioExposure as any)?.concentration ?? []).slice(0, 3)
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return (
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<Link to="/risk" className="card flex flex-col overflow-y-auto hover:border-slate-600/60 transition-all cursor-pointer"
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@@ -840,10 +839,22 @@ export default function Dashboard() {
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<div className={clsx('text-[10px] font-bold mt-0.5 flex items-center gap-2', alertCount > 0 ? 'text-red-400' : 'text-emerald-400')}>
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{alertCount > 0 ? `${alertCount} alert${alertCount > 1 ? 's' : ''}` : 'OK'}
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</div>
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{scenarioWarning && dominantScenario && (
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<div className="mt-1 text-[9px] leading-snug text-amber-400 bg-amber-900/10 border border-amber-700/20 rounded px-1.5 py-1">
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⚠ {dominantScenario.pct_of_portfolio.toFixed(0)}% du book sur le même pari macro
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(<span className="font-semibold">{dominantScenario.label}</span>)
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{scenarioRanking.length > 0 && (
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<div className="mt-1.5 space-y-1">
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<div className="text-[8px] uppercase tracking-wide text-slate-600">Portefeuille aligné sur</div>
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{scenarioRanking.map((s: any, i: number) => (
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<div key={s.key}>
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<div className="flex items-center justify-between text-[9px] mb-0.5">
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<span className="text-slate-300 truncate flex items-center gap-1">
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<span>{s.emoji}</span>{s.label}
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</span>
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<span className="font-mono font-bold shrink-0" style={{ color: s.color }}>{s.pct_of_portfolio}%</span>
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</div>
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<div className="h-1 bg-dark-700 rounded-full overflow-hidden">
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<div className="h-full rounded-full" style={{ width: `${Math.min(s.pct_of_portfolio, 100)}%`, background: s.color, opacity: i === 0 ? 1 : 0.6 }} />
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</div>
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</div>
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))}
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</div>
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)}
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{radarAxes.length > 0 && (
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@@ -145,11 +145,12 @@ function ScenarioExposureCard() {
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return (
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<div className="card">
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<div className="text-sm font-semibold text-white mb-1 flex items-center gap-2">
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<Layers className="w-4 h-4 text-purple-400" /> Concentration par scénario macro
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<Layers className="w-4 h-4 text-purple-400" /> Alignement du portefeuille sur le Régime Macro
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</div>
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<div className="text-[10px] text-slate-500 mb-3">
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Repricing Black-Scholes réel (pricing Saxo-first) de chaque position sous 5 scénarios —
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révèle quand plusieurs positions différentes sont en réalité le même pari répété.
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Repricing Black-Scholes réel (pricing Saxo-first) de chaque position sous les 8 scénarios du
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régime macro — révèle quand plusieurs positions différentes sont en réalité le même pari répété.
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Vue distincte des Risk Factors ci-dessous (thème/classe d'actif touché, pas le sens du pari).
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</div>
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{exp.warning && (
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@@ -160,7 +161,7 @@ function ScenarioExposureCard() {
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)}
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<div className="grid grid-cols-1 lg:grid-cols-2 gap-5">
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{/* Concentration bars */}
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{/* Concentration ranking */}
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<div>
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<div className="text-xs font-semibold text-slate-400 mb-2">
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% du book dont c'est le scénario le plus favorable
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@@ -169,13 +170,15 @@ function ScenarioExposureCard() {
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{concentration.map((c: any) => (
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<div key={c.key}>
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<div className="flex justify-between text-xs mb-1">
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<span className="text-slate-300">{c.label}</span>
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<span className={clsx('font-mono font-bold', c.pct_of_portfolio >= 60 ? 'text-amber-400' : 'text-slate-300')}>
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<span className="text-slate-300 flex items-center gap-1.5">
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<span>{c.emoji}</span>{c.label}
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</span>
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<span className="font-mono font-bold" style={{ color: c.color }}>
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{c.pct_of_portfolio}%
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</span>
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</div>
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<div className="h-2 bg-dark-700 rounded-full overflow-hidden">
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<div className="h-full rounded-full bg-purple-500/70" style={{ width: `${Math.min(c.pct_of_portfolio, 100)}%` }} />
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<div className="h-full rounded-full" style={{ width: `${Math.min(c.pct_of_portfolio, 100)}%`, background: c.color }} />
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</div>
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</div>
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))}
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@@ -189,7 +192,9 @@ function ScenarioExposureCard() {
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<tbody>
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{scenarios.map((s: any) => (
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<tr key={s.key} className="border-b border-slate-800/40">
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<td className="py-1.5 pr-3 text-slate-300">{s.label}</td>
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<td className="py-1.5 pr-3 text-slate-300 flex items-center gap-1.5">
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<span>{s.emoji}</span>{s.label}
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</td>
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<td className={clsx('py-1.5 text-right font-mono font-bold whitespace-nowrap',
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s.portfolio_pnl_pct >= 0 ? 'text-emerald-400' : 'text-red-400')}>
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{s.portfolio_pnl_pct >= 0 ? '+' : ''}{s.portfolio_pnl_pct}%
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