Add Almanac accuracy and snapshot APIs

app.py

@@ -28,6 +28,7 @@ try:
         RISK_HORIZON_MAP,
         RISK_HORIZON_LABELS,
         derive_investment_signal,
+        generate_rf_reasoning,
     )
     iris_app = IRIS_System()
 except ImportError as e:
@@ -111,6 +112,10 @@ SECTOR_PEERS = {
 _yf_info_cache = {}
 _YF_INFO_TTL = 300  # seconds
 _almanac_data = None
+_accuracy_data = None
+_accuracy_mtime = 0.0
+_iris_snapshot_cache = {"data": None, "ts": 0.0}
+_IRIS_SNAPSHOT_TTL = 300  # 5 minutes
 
 _ALMANAC_INDEX_KEY_MAP = {
     "djia": "dow",
@@ -331,6 +336,161 @@ def _load_almanac_data():
     return _almanac_data
 
 
+def _load_accuracy_data():
+    """Load accuracy_results.json with file-mtime caching."""
+    global _accuracy_data, _accuracy_mtime
+    path = PROJECT_ROOT / "data" / "almanac_2026" / "accuracy_results.json"
+    if not path.exists():
+        return None
+    mtime = path.stat().st_mtime
+    if _accuracy_data is not None and mtime <= _accuracy_mtime:
+        return _accuracy_data
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            _accuracy_data = json.load(f)
+        _accuracy_mtime = mtime
+        return _accuracy_data
+    except Exception as e:
+        print(f"[ACCURACY] Error loading: {e}")
+        return None
+
+
+def _iris_price_threshold(symbol: str) -> float:
+    token = str(symbol or "").strip().upper()
+    if "DJI" in token:
+        return 10000.0
+    if "IXIC" in token:
+        return 5000.0
+    return 400.0
+
+
+def _safe_float(value, default=0.0):
+    try:
+        if value is None or value == "":
+            return float(default)
+        return float(value)
+    except (TypeError, ValueError):
+        return float(default)
+
+
+def _iris_direction_from_pct_change(pct_change: float) -> str:
+    if pct_change > 0:
+        return "upward"
+    if pct_change < 0:
+        return "downward"
+    return "flat"
+
+
+def _iris_prediction_light(trend_label: str, sentiment_score=0.0) -> str:
+    normalized_trend = str(trend_label or "").upper()
+    sentiment = _safe_float(sentiment_score, 0.0)
+    if sentiment < -0.05 or "STRONG DOWNTREND" in normalized_trend:
+        return " RED (Risk Detected - Caution)"
+    if abs(sentiment) < 0.05 and "WEAK" in normalized_trend:
+        return " YELLOW (Neutral / Noise)"
+    return " GREEN (Safe to Proceed)"
+
+
+def _read_latest_iris_report(symbol: str):
+    """Read the latest valid IRIS report for the requested symbol from DATA_DIR."""
+    token = str(symbol or "").strip().upper()
+    bare = token.lstrip("^_")
+    filename_candidates = []
+    for candidate in (
+        f"{token}_report.json",
+        f"^{bare}_report.json",
+        f"_{bare}_report.json",
+        f"{bare}_report.json",
+    ):
+        path = DATA_DIR / candidate
+        if path not in filename_candidates:
+            filename_candidates.append(path)
+
+    min_price = _iris_price_threshold(token)
+
+    for path in filename_candidates:
+        if not path.exists():
+            continue
+        try:
+            with open(path, "r", encoding="utf-8") as f:
+                reports = json.load(f)
+            if not isinstance(reports, list):
+                reports = [reports]
+            for report in reversed(reports):
+                if not isinstance(report, dict):
+                    continue
+                current_price = _safe_float(report.get("market", {}).get("current_price"), 0.0)
+                if current_price < min_price:
+                    continue
+                horizon_1d = report.get("all_horizons", {}).get("1D", {})
+                meta = report.get("meta", {})
+                horizon_days = meta.get("horizon_days", 1) if isinstance(meta, dict) else 1
+                if not isinstance(horizon_1d, dict) and int(_safe_float(horizon_days, 1)) != 1:
+                    continue
+                return report
+        except Exception:
+            continue
+    return None
+
+
+def _format_iris_snapshot_entry(report: dict, label: str):
+    meta = report.get("meta", {}) if isinstance(report, dict) else {}
+    market = report.get("market", {}) if isinstance(report, dict) else {}
+    signals = report.get("signals", {}) if isinstance(report, dict) else {}
+    h1d = report.get("all_horizons", {}).get("1D", {}) if isinstance(report, dict) else {}
+    if not isinstance(meta, dict):
+        meta = {}
+    if not isinstance(market, dict):
+        market = {}
+    if not isinstance(signals, dict):
+        signals = {}
+    if not isinstance(h1d, dict):
+        h1d = {}
+
+    current_price = _safe_float(market.get("current_price"), 0.0)
+    predicted_price = (
+        market.get("predicted_price_next_session")
+        or h1d.get("predicted_price")
+        or market.get("predicted_price_horizon")
+    )
+    predicted_price = _safe_float(predicted_price, 0.0)
+
+    reasoning = h1d.get("iris_reasoning") or signals.get("iris_reasoning") or {}
+    if not isinstance(reasoning, dict):
+        reasoning = {}
+    pct_change = reasoning.get("pct_change")
+    if pct_change in (None, "") and current_price:
+        pct_change = ((predicted_price - current_price) / current_price) * 100
+    pct_change = round(_safe_float(pct_change, 0.0), 2)
+
+    direction = str(reasoning.get("direction", "")).strip().lower()
+    if not direction:
+        direction = _iris_direction_from_pct_change(pct_change)
+
+    top_factors = reasoning.get("top_factors", [])
+    if not isinstance(top_factors, list):
+        top_factors = []
+
+    return {
+        "available": True,
+        "label": label,
+        "symbol": str(meta.get("symbol") or meta.get("source_symbol") or "").strip(),
+        "session_date": str(meta.get("market_session_date", "")).strip(),
+        "generated_at": str(meta.get("generated_at", "")).strip(),
+        "current_price": current_price or None,
+        "predicted_price": predicted_price or None,
+        "trend_label": str(h1d.get("trend_label") or signals.get("trend_label", "")).strip(),
+        "investment_signal": str(h1d.get("investment_signal") or signals.get("investment_signal", "")).strip(),
+        "check_engine_light": str(signals.get("check_engine_light", "")).strip(),
+        "pct_change": pct_change,
+        "direction": direction,
+        "top_factors": top_factors,
+        "model_confidence": h1d.get("model_confidence") or signals.get("model_confidence"),
+        "sentiment_score": _safe_float(signals.get("sentiment_score"), 0.0),
+        "source": "report_snapshot",
+    }
+
+
 def _get_related_tickers(ticker, count=7):
     """Return a list of related tickers based on the sector of the given ticker."""
     fallback = ["AAPL", "MSFT", "GOOG", "AMZN", "NVDA", "META", "TSLA"]
@@ -576,6 +736,254 @@ def almanac_week():
         }
     )
 
+
+# --- Almanac Accuracy Tracking API ---
+
+def _accuracy_unavailable_response():
+    return jsonify(
+        {
+            "available": False,
+            "message": "Run scripts/seed_accuracy.py to generate accuracy data.",
+        }
+    )
+
+
+def _accuracy_pct(hits, total):
+    if not total:
+        return 0.0
+    return round((hits / total) * 100, 1)
+
+
+@app.route('/api/almanac/accuracy')
+def almanac_accuracy():
+    """Return almanac historic accuracy results."""
+    data = _load_accuracy_data()
+    if data is None:
+        return _accuracy_unavailable_response()
+
+    daily = data.get("daily", {})
+    date_param = str(request.args.get("date", "") or "").strip()
+    from_param = str(request.args.get("from", "") or "").strip()
+    to_param = str(request.args.get("to", "") or "").strip()
+
+    if date_param:
+        entry = daily.get(date_param)
+        if entry is None:
+            return jsonify({"error": f"No accuracy data for {date_param}"}), 404
+        return jsonify(entry)
+
+    if from_param and to_param:
+        filtered = {k: v for k, v in daily.items() if from_param <= k <= to_param}
+        return jsonify({"from": from_param, "to": to_param, "daily": filtered})
+
+    return jsonify({"daily": daily})
+
+
+@app.route('/api/almanac/accuracy/week')
+def almanac_accuracy_week():
+    """Return weekly accuracy results for the requested week."""
+    data = _load_accuracy_data()
+    if data is None:
+        return _accuracy_unavailable_response()
+
+    start = str(request.args.get("start", "") or "").strip()
+    if not start:
+        return jsonify({"error": "start query parameter is required"}), 400
+
+    try:
+        week_key = datetime.strptime(start, "%Y-%m-%d").strftime("%Y-W%W")
+    except ValueError:
+        return jsonify({"error": "Invalid start date. Expected YYYY-MM-DD"}), 400
+
+    weekly_entry = (data.get("weekly") or {}).get(week_key)
+    if weekly_entry is None:
+        return jsonify({"error": f"No weekly accuracy found for {week_key}"}), 404
+    return jsonify(weekly_entry)
+
+
+@app.route('/api/almanac/accuracy/month')
+def almanac_accuracy_month():
+    """Return monthly accuracy results for the requested month."""
+    data = _load_accuracy_data()
+    if data is None:
+        return _accuracy_unavailable_response()
+
+    month_key = str(request.args.get("month", "") or "").strip()
+    if not month_key:
+        return jsonify({"error": "month query parameter is required"}), 400
+
+    monthly_entry = (data.get("monthly") or {}).get(month_key)
+    if monthly_entry is None:
+        return jsonify({"error": f"No monthly accuracy found for {month_key}"}), 404
+    return jsonify(monthly_entry)
+
+
+@app.route('/api/almanac/accuracy/summary')
+def almanac_accuracy_summary():
+    """Return aggregate historic accuracy metrics."""
+    data = _load_accuracy_data()
+    if data is None:
+        return _accuracy_unavailable_response()
+
+    monthly = data.get("monthly") or {}
+    daily = data.get("daily") or {}
+
+    overall_hits = sum(int(month.get("hits", 0)) for month in monthly.values())
+    overall_total = sum(int(month.get("total_calls", 0)) for month in monthly.values())
+
+    per_index = {}
+    for index_key in ("dow", "sp500", "nasdaq"):
+        hits = sum(int(month.get(index_key, {}).get("hits", 0)) for month in monthly.values())
+        total = sum(int(month.get(index_key, {}).get("total", 0)) for month in monthly.values())
+        per_index[index_key] = {
+            "hits": hits,
+            "total": total,
+            "pct": _accuracy_pct(hits, total),
+        }
+
+    return jsonify(
+        {
+            "overall": {
+                "hits": overall_hits,
+                "total_calls": overall_total,
+                "accuracy": _accuracy_pct(overall_hits, overall_total),
+            },
+            "monthly": monthly,
+            "per_index": per_index,
+            "last_scored_date": max(daily.keys()) if daily else None,
+            "total_days": len(daily),
+        }
+    )
+
+
+# --- IRIS Snapshot for Almanac Dashboard ---
+
+@app.route('/api/almanac/iris-snapshot')
+def almanac_iris_snapshot():
+    """Return the latest cached IRIS index predictions from on-disk report files."""
+    now = time.time()
+    if (
+        _iris_snapshot_cache["data"] is not None
+        and (now - _iris_snapshot_cache["ts"]) < _IRIS_SNAPSHOT_TTL
+    ):
+        return jsonify(_iris_snapshot_cache["data"])
+
+    symbols = {
+        "spy": {"file_symbol": "SPY", "label": "SPY (S&P 500 ETF)"},
+        "dji": {"file_symbol": "^DJI", "label": "Dow Jones"},
+        "gspc": {"file_symbol": "^GSPC", "label": "S&P 500 Index"},
+        "ixic": {"file_symbol": "^IXIC", "label": "NASDAQ"},
+    }
+
+    result = {
+        "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "indices": {},
+    }
+
+    for key, info in symbols.items():
+        report = _read_latest_iris_report(info["file_symbol"])
+        if not report:
+            result["indices"][key] = {
+                "available": False,
+                "label": info["label"],
+            }
+            continue
+        result["indices"][key] = _format_iris_snapshot_entry(report, info["label"])
+
+    _iris_snapshot_cache["data"] = result
+    _iris_snapshot_cache["ts"] = now
+    return jsonify(result)
+
+
+@app.route('/api/almanac/iris-refresh')
+def almanac_iris_refresh():
+    """Run lightweight 1D IRIS predictions for the dashboard's major indices."""
+    if not iris_app:
+        return jsonify({"error": "IRIS not initialized"}), 500
+
+    tickers = {
+        "spy": {"ticker": "SPY", "label": "SPY (S&P 500 ETF)"},
+        "dji": {"ticker": "^DJI", "label": "Dow Jones"},
+        "gspc": {"ticker": "^GSPC", "label": "S&P 500 Index"},
+        "ixic": {"ticker": "^IXIC", "label": "NASDAQ"},
+    }
+    generated_at = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+    result = {"generated_at": generated_at, "indices": {}}
+
+    for key, info in tickers.items():
+        ticker = info["ticker"]
+        try:
+            data = iris_app.get_market_data(ticker)
+            if not data:
+                result["indices"][key] = {
+                    "available": False,
+                    "label": info["label"],
+                }
+                continue
+
+            trend_label, predicted_price, trajectory, traj_upper, traj_lower, rf_model, model_confidence = iris_app.predict_trend(
+                data,
+                sentiment_score=0.0,
+                horizon_days=1,
+            )
+
+            current_price = _safe_float(data.get("current_price"), 0.0)
+            pct_change = ((predicted_price - current_price) / current_price * 100) if current_price else 0.0
+            history_df = data.get("history_df")
+            last_rsi = 50.0
+            session_date = time.strftime("%Y-%m-%d")
+            if history_df is not None and "rsi_14" in history_df.columns and len(history_df):
+                last_rsi = float(history_df["rsi_14"].iloc[-1])
+            if history_df is not None and len(history_df):
+                try:
+                    session_date = str(pd.Timestamp(history_df.index[-1]).date())
+                except Exception:
+                    session_date = time.strftime("%Y-%m-%d")
+
+            investment_signal = derive_investment_signal(pct_change, 0.0, last_rsi, 1)
+            reasoning = {}
+            if rf_model is not None:
+                try:
+                    reasoning = generate_rf_reasoning(
+                        rf_model,
+                        None,
+                        current_price,
+                        predicted_price,
+                        "1 Day",
+                    )
+                except Exception:
+                    reasoning = {}
+
+            result["indices"][key] = {
+                "available": True,
+                "label": info["label"],
+                "symbol": ticker,
+                "session_date": session_date,
+                "generated_at": generated_at,
+                "current_price": round(current_price, 6),
+                "predicted_price": round(float(predicted_price), 6),
+                "trend_label": trend_label,
+                "investment_signal": investment_signal,
+                "check_engine_light": _iris_prediction_light(trend_label, 0.0).strip(),
+                "pct_change": round(float(pct_change), 2),
+                "direction": _iris_direction_from_pct_change(pct_change),
+                "top_factors": reasoning.get("top_factors", []) if isinstance(reasoning, dict) else [],
+                "model_confidence": round(float(model_confidence), 1),
+                "sentiment_score": 0.0,
+                "source": "live_rf_prediction",
+            }
+        except Exception as e:
+            result["indices"][key] = {
+                "available": False,
+                "label": info["label"],
+                "error": str(e),
+            }
+
+    _iris_snapshot_cache["data"] = None
+    _iris_snapshot_cache["ts"] = 0.0
+    return jsonify(result)
+
+
 @app.route('/api/history/<ticker>', methods=['GET'])
 def get_history(ticker):
     """Return lightweight market history points directly from yfinance for chart rendering."""

data/almanac_2026/accuracy_results.json

@@ -0,0 +1,3118 @@
+{
+  "meta": {
+    "last_updated": "2026-04-05T07:26:51.578106Z",
+    "total_days_scored": 63,
+    "data_range": {
+      "from": "2026-01-02",
+      "to": "2026-04-02"
+    },
+    "source": "Historic CSV backtest via scripts/seed_accuracy.py"
+  },
+  "daily": {
+    "2026-01-02": {
+      "actual": {
+        "dji": 48382.39,
+        "sp500": 6858.47,
+        "nasdaq": 23235.63
+      },
+      "prev_close": {
+        "dji": 48063.29,
+        "sp500": 6845.5,
+        "nasdaq": 23241.99
+      },
+      "pct_change": {
+        "dji": 0.006639,
+        "sp500": 0.001895,
+        "nasdaq": -0.000274
+      },
+      "almanac_scores": {
+        "d": 66.7,
+        "s": 52.4,
+        "n": 61.9
+      },
+      "results": {
+        "d": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        },
+        "s": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        },
+        "n": {
+          "verdict": "MISS",
+          "predicted": "UP",
+          "actual": "DOWN"
+        }
+      },
+      "hits": 2,
+      "total_calls": 3,
+      "context": "First Trading Day of Year, NASDAQ Up 18 of Last 28"
+    },
+    "2026-01-05": {
+      "actual": {
+        "dji": 48977.18,
+        "sp500": 6902.05,
+        "nasdaq": 23395.82
+      },
+      "prev_close": {
+        "dji": 48382.39,
+        "sp500": 6858.47,
+        "nasdaq": 23235.63
+      },
+      "pct_change": {
+        "dji": 0.012294,
+        "sp500": 0.006354,
+        "nasdaq": 0.006894
+      },
+      "almanac_scores": {
+        "d": 61.9,
+        "s": 47.6,
+        "n": 38.1
+      },
+      "results": {
+        "d": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        },
+        "s": {
+          "verdict": "MISS",
+          "predicted": "DOWN",
+          "actual": "UP"
+        },
+        "n": {
+          "verdict": "MISS",
+          "predicted": "DOWN",
+          "actual": "UP"
+        }
+      },
+      "hits": 1,
+      "total_calls": 3,
+      "context": "Second Trading Day of the Year, Dow Up 21 of Last 32; Santa Claus Rally Ends"
+    },
+    "2026-01-06": {
+      "actual": {
+        "dji": 49462.08,
+        "sp500": 6944.82,
+        "nasdaq": 23547.17
+      },
+      "prev_close": {
+        "dji": 48977.18,
+        "sp500": 6902.05,
+        "nasdaq": 23395.82
+      },
+      "pct_change": {
+        "dji": 0.009901,
+        "sp500": 0.006197,
+        "nasdaq": 0.006469
+      },
+      "almanac_scores": {
+        "d": 47.6,
+        "s": 52.4,
+        "n": 47.6
+      },
+      "results": {
+        "d": {
+          "verdict": "MISS",
+          "predicted": "DOWN",
+          "actual": "UP"
+        },
+        "s": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        },
+        "n": {
+          "verdict": "MISS",
+          "predicted": "DOWN",
+          "actual": "UP"
+        }
+      },
+      "hits": 1,
+      "total_calls": 3,
+      "context": ""
+    },
+    "2026-01-07": {
+      "actual": {
+        "dji": 48996.08,
+        "sp500": 6920.93,
+        "nasdaq": 23584.27
+      },
+      "prev_close": {
+        "dji": 49462.08,
+        "sp500": 6944.82,
+        "nasdaq": 23547.17
+      },
+      "pct_change": {
+        "dji": -0.009421,
+        "sp500": -0.00344,
+        "nasdaq": 0.001576
+      },
+      "almanac_scores": {
+        "d": 61.9,
+        "s": 66.7,
+        "n": 61.9
+      },
+      "results": {
+        "d": {
+          "verdict": "MISS",
+          "predicted": "UP",
+          "actual": "DOWN"
+        },
+        "s": {
+          "verdict": "MISS",
+          "predicted": "UP",
+          "actual": "DOWN"
+        },
+        "n": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        }
+      },
+      "hits": 1,
+      "total_calls": 3,
+      "context": ""
+    },
+    "2026-01-08": {
+      "actual": {
+        "dji": 49266.11,
+        "sp500": 6921.46,
+        "nasdaq": 23480.02
+      },
+      "prev_close": {
+        "dji": 48996.08,
+        "sp500": 6920.93,
+        "nasdaq": 23584.27
+      },
+      "pct_change": {
+        "dji": 0.005511,
+        "sp500": 7.7e-05,
+        "nasdaq": -0.00442
+      },
+      "almanac_scores": {
+        "d": 42.9,
+        "s": 52.4,
+        "n": 71.4
+      },
+      "results": {
+        "d": {
+          "verdict": "MISS",
+          "predicted": "DOWN",
+          "actual": "UP"
+        },
+        "s": {
+          "verdict": "HIT",
+          "predicted": "UP",
+          "actual": "UP"
+        },
+        "n": {
+          "verdict": "MISS",
+          "predicted": "UP",
+          "actual": "DOWN"
+        }
+      },
+      "hits": 1,
+      "total_calls": 3,
+      "context": "January's First Five Days Act as an Early Warning"
+    },
+    "2026-01-09": {
+      "actual": {
+        "dji": 49504.07,
+        "sp500": 6966.28,
+        "nasdaq": 23671.35
+      },
+      "prev_close": {
+        "dji": 49266.11,
+        "sp500": 6921.46,
+        "nasdaq": 23480.02
+      },
+      "pct_change": {
+        "dji": 0.00483,
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+      }
+    },
+    "2026-W02": {
+      "hits": 5,
+      "total_calls": 15,
+      "accuracy": 33.3,
+      "dates": [
+        "2026-01-12",
+        "2026-01-13",
+        "2026-01-14",
+        "2026-01-15",
+        "2026-01-16"
+      ],
+      "dow": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      },
+      "sp500": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      },
+      "nasdaq": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      }
+    },
+    "2026-W03": {
+      "hits": 10,
+      "total_calls": 12,
+      "accuracy": 83.3,
+      "dates": [
+        "2026-01-20",
+        "2026-01-21",
+        "2026-01-22",
+        "2026-01-23"
+      ],
+      "dow": {
+        "hits": 3,
+        "total": 4,
+        "pct": 75.0
+      },
+      "sp500": {
+        "hits": 4,
+        "total": 4,
+        "pct": 100.0
+      },
+      "nasdaq": {
+        "hits": 3,
+        "total": 4,
+        "pct": 75.0
+      }
+    },
+    "2026-W04": {
+      "hits": 11,
+      "total_calls": 15,
+      "accuracy": 73.3,
+      "dates": [
+        "2026-01-26",
+        "2026-01-27",
+        "2026-01-28",
+        "2026-01-29",
+        "2026-01-30"
+      ],
+      "dow": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      },
+      "sp500": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      },
+      "nasdaq": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      }
+    },
+    "2026-W05": {
+      "hits": 8,
+      "total_calls": 15,
+      "accuracy": 53.3,
+      "dates": [
+        "2026-02-02",
+        "2026-02-03",
+        "2026-02-04",
+        "2026-02-05",
+        "2026-02-06"
+      ],
+      "dow": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      },
+      "sp500": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      },
+      "nasdaq": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      }
+    },
+    "2026-W06": {
+      "hits": 4,
+      "total_calls": 15,
+      "accuracy": 26.7,
+      "dates": [
+        "2026-02-09",
+        "2026-02-10",
+        "2026-02-11",
+        "2026-02-12",
+        "2026-02-13"
+      ],
+      "dow": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      },
+      "sp500": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      },
+      "nasdaq": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      }
+    },
+    "2026-W07": {
+      "hits": 8,
+      "total_calls": 12,
+      "accuracy": 66.7,
+      "dates": [
+        "2026-02-17",
+        "2026-02-18",
+        "2026-02-19",
+        "2026-02-20"
+      ],
+      "dow": {
+        "hits": 3,
+        "total": 4,
+        "pct": 75.0
+      },
+      "sp500": {
+        "hits": 3,
+        "total": 4,
+        "pct": 75.0
+      },
+      "nasdaq": {
+        "hits": 2,
+        "total": 4,
+        "pct": 50.0
+      }
+    },
+    "2026-W08": {
+      "hits": 11,
+      "total_calls": 15,
+      "accuracy": 73.3,
+      "dates": [
+        "2026-02-23",
+        "2026-02-24",
+        "2026-02-25",
+        "2026-02-26",
+        "2026-02-27"
+      ],
+      "dow": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      },
+      "sp500": {
+        "hits": 5,
+        "total": 5,
+        "pct": 100.0
+      },
+      "nasdaq": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      }
+    },
+    "2026-W09": {
+      "hits": 12,
+      "total_calls": 15,
+      "accuracy": 80.0,
+      "dates": [
+        "2026-03-02",
+        "2026-03-03",
+        "2026-03-04",
+        "2026-03-05",
+        "2026-03-06"
+      ],
+      "dow": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      },
+      "sp500": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      },
+      "nasdaq": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      }
+    },
+    "2026-W10": {
+      "hits": 4,
+      "total_calls": 15,
+      "accuracy": 26.7,
+      "dates": [
+        "2026-03-09",
+        "2026-03-10",
+        "2026-03-11",
+        "2026-03-12",
+        "2026-03-13"
+      ],
+      "dow": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      },
+      "sp500": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      },
+      "nasdaq": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      }
+    },
+    "2026-W11": {
+      "hits": 4,
+      "total_calls": 15,
+      "accuracy": 26.7,
+      "dates": [
+        "2026-03-16",
+        "2026-03-17",
+        "2026-03-18",
+        "2026-03-19",
+        "2026-03-20"
+      ],
+      "dow": {
+        "hits": 2,
+        "total": 5,
+        "pct": 40.0
+      },
+      "sp500": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      },
+      "nasdaq": {
+        "hits": 1,
+        "total": 5,
+        "pct": 20.0
+      }
+    },
+    "2026-W12": {
+      "hits": 11,
+      "total_calls": 15,
+      "accuracy": 73.3,
+      "dates": [
+        "2026-03-23",
+        "2026-03-24",
+        "2026-03-25",
+        "2026-03-26",
+        "2026-03-27"
+      ],
+      "dow": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      },
+      "sp500": {
+        "hits": 4,
+        "total": 5,
+        "pct": 80.0
+      },
+      "nasdaq": {
+        "hits": 3,
+        "total": 5,
+        "pct": 60.0
+      }
+    },
+    "2026-W13": {
+      "hits": 7,
+      "total_calls": 12,
+      "accuracy": 58.3,
+      "dates": [
+        "2026-03-30",
+        "2026-03-31",
+        "2026-04-01",
+        "2026-04-02"
+      ],
+      "dow": {
+        "hits": 2,
+        "total": 4,
+        "pct": 50.0
+      },
+      "sp500": {
+        "hits": 2,
+        "total": 4,
+        "pct": 50.0
+      },
+      "nasdaq": {
+        "hits": 3,
+        "total": 4,
+        "pct": 75.0
+      }
+    }
+  },
+  "monthly": {
+    "2026-01": {
+      "hits": 35,
+      "total_calls": 60,
+      "accuracy": 58.3,
+      "dow": {
+        "hits": 11,
+        "total": 20,
+        "pct": 55.0
+      },
+      "sp500": {
+        "hits": 13,
+        "total": 20,
+        "pct": 65.0
+      },
+      "nasdaq": {
+        "hits": 11,
+        "total": 20,
+        "pct": 55.0
+      },
+      "trading_days": 20
+    },
+    "2026-02": {
+      "hits": 31,
+      "total_calls": 57,
+      "accuracy": 54.4,
+      "dow": {
+        "hits": 9,
+        "total": 19,
+        "pct": 47.4
+      },
+      "sp500": {
+        "hits": 13,
+        "total": 19,
+        "pct": 68.4
+      },
+      "nasdaq": {
+        "hits": 9,
+        "total": 19,
+        "pct": 47.4
+      },
+      "trading_days": 19
+    },
+    "2026-03": {
+      "hits": 33,
+      "total_calls": 66,
+      "accuracy": 50.0,
+      "dow": {
+        "hits": 12,
+        "total": 22,
+        "pct": 54.5
+      },
+      "sp500": {
+        "hits": 10,
+        "total": 22,
+        "pct": 45.5
+      },
+      "nasdaq": {
+        "hits": 11,
+        "total": 22,
+        "pct": 50.0
+      },
+      "trading_days": 22
+    },
+    "2026-04": {
+      "hits": 5,
+      "total_calls": 6,
+      "accuracy": 83.3,
+      "dow": {
+        "hits": 1,
+        "total": 2,
+        "pct": 50.0
+      },
+      "sp500": {
+        "hits": 2,
+        "total": 2,
+        "pct": 100.0
+      },
+      "nasdaq": {
+        "hits": 2,
+        "total": 2,
+        "pct": 100.0
+      },
+      "trading_days": 2
+    }
+  }
+}

tests/test_almanac_accuracy_api.py

@@ -0,0 +1,196 @@
+"""Tests for the Almanac historic accuracy API routes."""
+
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import app as app_module
+
+
+ACCURACY_FIXTURE = {
+    "meta": {
+        "last_updated": "2026-04-05T12:00:00Z",
+        "total_days_scored": 3,
+        "data_range": {"from": "2026-01-02", "to": "2026-01-06"},
+        "source": "Historic CSV backtest via scripts/seed_accuracy.py",
+    },
+    "daily": {
+        "2026-01-02": {
+            "actual": {"dji": 101.0, "sp500": 199.0, "nasdaq": 300.0},
+            "prev_close": {"dji": 100.0, "sp500": 200.0, "nasdaq": 300.0},
+            "pct_change": {"dji": 0.01, "sp500": -0.005, "nasdaq": 0.0},
+            "almanac_scores": {"d": 60.0, "s": 40.0, "n": 50.0},
+            "results": {
+                "d": {"verdict": "HIT", "predicted": "UP", "actual": "UP"},
+                "s": {"verdict": "HIT", "predicted": "DOWN", "actual": "DOWN"},
+                "n": {"verdict": None, "predicted": None, "actual": "FLAT"},
+            },
+            "hits": 2,
+            "total_calls": 2,
+            "context": "Opening session",
+        },
+        "2026-01-05": {
+            "actual": {"dji": 100.0, "sp500": 200.0, "nasdaq": 303.0},
+            "prev_close": {"dji": 101.0, "sp500": 199.0, "nasdaq": 300.0},
+            "pct_change": {"dji": -0.009901, "sp500": 0.005025, "nasdaq": 0.01},
+            "almanac_scores": {"d": 45.0, "s": 55.0, "n": 70.0},
+            "results": {
+                "d": {"verdict": "HIT", "predicted": "DOWN", "actual": "DOWN"},
+                "s": {"verdict": "HIT", "predicted": "UP", "actual": "UP"},
+                "n": {"verdict": "HIT", "predicted": "UP", "actual": "UP"},
+            },
+            "hits": 3,
+            "total_calls": 3,
+            "context": "",
+        },
+        "2026-01-06": {
+            "actual": {"dji": 102.0, "sp500": 198.0, "nasdaq": 300.0},
+            "prev_close": {"dji": 100.0, "sp500": 200.0, "nasdaq": 303.0},
+            "pct_change": {"dji": 0.02, "sp500": -0.01, "nasdaq": -0.009901},
+            "almanac_scores": {"d": 80.0, "s": 20.0, "n": 60.0},
+            "results": {
+                "d": {"verdict": "HIT", "predicted": "UP", "actual": "UP"},
+                "s": {"verdict": "HIT", "predicted": "DOWN", "actual": "DOWN"},
+                "n": {"verdict": "MISS", "predicted": "UP", "actual": "DOWN"},
+            },
+            "hits": 2,
+            "total_calls": 3,
+            "context": "Momentum test",
+        },
+    },
+    "weekly": {
+        "2026-W00": {
+            "dates": ["2026-01-02"],
+            "hits": 2,
+            "total_calls": 2,
+            "accuracy": 100.0,
+            "dow": {"hits": 1, "total": 1, "pct": 100.0},
+            "sp500": {"hits": 1, "total": 1, "pct": 100.0},
+            "nasdaq": {"hits": 0, "total": 0, "pct": 0.0},
+        },
+        "2026-W01": {
+            "dates": ["2026-01-05", "2026-01-06"],
+            "hits": 5,
+            "total_calls": 6,
+            "accuracy": 83.3,
+            "dow": {"hits": 2, "total": 2, "pct": 100.0},
+            "sp500": {"hits": 2, "total": 2, "pct": 100.0},
+            "nasdaq": {"hits": 1, "total": 2, "pct": 50.0},
+        },
+    },
+    "monthly": {
+        "2026-01": {
+            "hits": 7,
+            "total_calls": 8,
+            "accuracy": 87.5,
+            "dow": {"hits": 3, "total": 3, "pct": 100.0},
+            "sp500": {"hits": 3, "total": 3, "pct": 100.0},
+            "nasdaq": {"hits": 1, "total": 2, "pct": 50.0},
+            "trading_days": 3,
+        }
+    },
+}
+
+
+class TestAlmanacAccuracyAPI(unittest.TestCase):
+    def setUp(self):
+        app_module.app.config["TESTING"] = True
+        self.client = app_module.app.test_client()
+        app_module._accuracy_data = None
+        app_module._accuracy_mtime = 0.0
+
+    def tearDown(self):
+        app_module._accuracy_data = None
+        app_module._accuracy_mtime = 0.0
+
+    def make_root(self, name: str) -> Path:
+        root = Path(__file__).resolve().parent.parent / "tmp_feedback_test_main" / name
+        shutil.rmtree(root, ignore_errors=True)
+        root.mkdir(parents=True, exist_ok=True)
+        return root
+
+    def write_accuracy_fixture(self, root: Path) -> None:
+        path = root / "data" / "almanac_2026" / "accuracy_results.json"
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(ACCURACY_FIXTURE, indent=2), encoding="utf-8")
+
+    def test_accuracy_endpoint_reports_unavailable_when_seed_file_is_missing(self):
+        temp_root = self.make_root("accuracy_api_unavailable")
+        try:
+            with patch.object(app_module, "PROJECT_ROOT", temp_root):
+                resp = self.client.get("/api/almanac/accuracy")
+        finally:
+            shutil.rmtree(temp_root, ignore_errors=True)
+
+        self.assertEqual(resp.status_code, 200)
+        data = resp.get_json()
+        self.assertEqual(data["available"], False)
+        self.assertIn("seed_accuracy.py", data["message"])
+
+    def test_accuracy_endpoint_supports_all_single_date_and_range_queries(self):
+        temp_root = self.make_root("accuracy_api_daily")
+        self.write_accuracy_fixture(temp_root)
+        try:
+            with patch.object(app_module, "PROJECT_ROOT", temp_root):
+                all_resp = self.client.get("/api/almanac/accuracy")
+                day_resp = self.client.get("/api/almanac/accuracy?date=2026-01-06")
+                range_resp = self.client.get("/api/almanac/accuracy?from=2026-01-05&to=2026-01-06")
+                missing_resp = self.client.get("/api/almanac/accuracy?date=2026-01-07")
+        finally:
+            shutil.rmtree(temp_root, ignore_errors=True)
+
+        self.assertEqual(all_resp.status_code, 200)
+        self.assertIn("2026-01-02", all_resp.get_json()["daily"])
+        self.assertEqual(day_resp.status_code, 200)
+        self.assertEqual(day_resp.get_json()["results"]["n"]["verdict"], "MISS")
+        self.assertEqual(range_resp.status_code, 200)
+        self.assertEqual(sorted(range_resp.get_json()["daily"].keys()), ["2026-01-05", "2026-01-06"])
+        self.assertEqual(missing_resp.status_code, 404)
+
+    def test_accuracy_week_and_month_routes_return_expected_records(self):
+        temp_root = self.make_root("accuracy_api_periods")
+        self.write_accuracy_fixture(temp_root)
+        try:
+            with patch.object(app_module, "PROJECT_ROOT", temp_root):
+                week_resp = self.client.get("/api/almanac/accuracy/week?start=2026-01-05")
+                month_resp = self.client.get("/api/almanac/accuracy/month?month=2026-01")
+                invalid_week_resp = self.client.get("/api/almanac/accuracy/week?start=not-a-date")
+        finally:
+            shutil.rmtree(temp_root, ignore_errors=True)
+
+        self.assertEqual(week_resp.status_code, 200)
+        self.assertEqual(week_resp.get_json()["hits"], 5)
+        self.assertEqual(month_resp.status_code, 200)
+        self.assertEqual(month_resp.get_json()["trading_days"], 3)
+        self.assertEqual(invalid_week_resp.status_code, 400)
+
+    def test_accuracy_summary_aggregates_monthly_and_per_index_totals(self):
+        temp_root = self.make_root("accuracy_api_summary")
+        self.write_accuracy_fixture(temp_root)
+        try:
+            with patch.object(app_module, "PROJECT_ROOT", temp_root):
+                resp = self.client.get("/api/almanac/accuracy/summary")
+        finally:
+            shutil.rmtree(temp_root, ignore_errors=True)
+
+        self.assertEqual(resp.status_code, 200)
+        data = resp.get_json()
+        self.assertEqual(data["overall"]["hits"], 7)
+        self.assertEqual(data["overall"]["total_calls"], 8)
+        self.assertEqual(data["overall"]["accuracy"], 87.5)
+        self.assertEqual(data["per_index"]["dow"]["pct"], 100.0)
+        self.assertEqual(data["per_index"]["nasdaq"]["pct"], 50.0)
+        self.assertEqual(data["last_scored_date"], "2026-01-06")
+        self.assertEqual(data["total_days"], 3)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_almanac_iris_api.py

@@ -0,0 +1,228 @@
+"""Tests for the Almanac IRIS snapshot and refresh API routes."""
+
+from __future__ import annotations
+
+import json
+import os
+import shutil
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import app as app_module
+import iris_mvp
+
+
+def build_report(
+    symbol: str,
+    *,
+    current_price: float,
+    predicted_price: float,
+    session_date: str,
+    generated_at: str,
+    trend_label: str = "STRONG UPTREND",
+    investment_signal: str = "BUY",
+    check_engine_light: str = " GREEN (Safe to Proceed)",
+    pct_change: float = 1.25,
+    direction: str = "upward",
+):
+    return {
+        "meta": {
+            "symbol": symbol,
+            "source_symbol": symbol,
+            "generated_at": generated_at,
+            "market_session_date": session_date,
+            "horizon_days": 1,
+        },
+        "market": {
+            "current_price": current_price,
+            "predicted_price_next_session": predicted_price,
+            "predicted_price_horizon": predicted_price,
+        },
+        "signals": {
+            "trend_label": trend_label,
+            "investment_signal": investment_signal,
+            "check_engine_light": check_engine_light,
+            "sentiment_score": 0.0,
+            "iris_reasoning": {
+                "pct_change": pct_change,
+                "direction": direction,
+                "top_factors": ["SMA(10)", "Day Trend", "SMA(20)"],
+            },
+            "model_confidence": 92.5,
+        },
+        "all_horizons": {
+            "1D": {
+                "predicted_price": predicted_price,
+                "trend_label": trend_label,
+                "investment_signal": investment_signal,
+                "iris_reasoning": {
+                    "pct_change": pct_change,
+                    "direction": direction,
+                    "top_factors": ["SMA(10)", "Day Trend", "SMA(20)"],
+                },
+                "model_confidence": 92.5,
+            }
+        },
+    }
+
+
+class DummyModel:
+    feature_importances_ = np.array([0.4, 0.3, 0.2, 0.1])
+
+
+class FakeIrisApp:
+    def get_market_data(self, ticker):
+        history_df = pd.DataFrame(
+            {"rsi_14": [48.0, 56.0]},
+            index=pd.to_datetime(["2026-04-01", "2026-04-02"]),
+        )
+        base_prices = {
+            "SPY": 650.0,
+            "^DJI": 46000.0,
+            "^GSPC": 6550.0,
+            "^IXIC": 21800.0,
+        }
+        return {
+            "current_price": base_prices[ticker],
+            "history_df": history_df,
+        }
+
+    def predict_trend(self, data, sentiment_score, horizon_days=1):
+        current_price = float(data["current_price"])
+        predicted_price = current_price * 1.01
+        return (
+            "STRONG UPTREND",
+            predicted_price,
+            [predicted_price],
+            [predicted_price * 1.02],
+            [predicted_price * 0.98],
+            DummyModel(),
+            88.4,
+        )
+
+
+class TestAlmanacIrisAPI(unittest.TestCase):
+    def setUp(self):
+        app_module.app.config["TESTING"] = True
+        self.client = app_module.app.test_client()
+        self.original_data_dir = app_module.DATA_DIR
+        self.original_iris_app = app_module.iris_app
+        app_module._iris_snapshot_cache = {"data": None, "ts": 0.0}
+
+    def tearDown(self):
+        app_module.DATA_DIR = self.original_data_dir
+        app_module.iris_app = self.original_iris_app
+        app_module._iris_snapshot_cache = {"data": None, "ts": 0.0}
+
+    def make_root(self, name: str) -> Path:
+        root = Path(__file__).resolve().parent.parent / "tmp_feedback_test_main" / name
+        shutil.rmtree(root, ignore_errors=True)
+        root.mkdir(parents=True, exist_ok=True)
+        return root
+
+    def test_iris_snapshot_reads_latest_valid_reports_from_data_dir(self):
+        temp_root = self.make_root("almanac_iris_snapshot")
+        try:
+            (temp_root / "SPY_report.json").write_text(
+                json.dumps(
+                    [
+                        build_report(
+                            "SPY",
+                            current_price=640.0,
+                            predicted_price=646.4,
+                            session_date="2026-04-01",
+                            generated_at="2026-04-01T12:00:00Z",
+                        )
+                    ],
+                    indent=2,
+                ),
+                encoding="utf-8",
+            )
+            # The latest entry is intentionally invalid due to low price and must be skipped.
+            (temp_root / "^DJI_report.json").write_text(
+                json.dumps(
+                    [
+                        build_report(
+                            "^DJI",
+                            current_price=46200.0,
+                            predicted_price=46350.0,
+                            session_date="2026-04-02",
+                            generated_at="2026-04-02T12:00:00Z",
+                            investment_signal="BUY",
+                        ),
+                        build_report(
+                            "DJI",
+                            current_price=88.0,
+                            predicted_price=91.0,
+                            session_date="2026-04-03",
+                            generated_at="2026-04-03T12:00:00Z",
+                            investment_signal="BUY",
+                        ),
+                    ],
+                    indent=2,
+                ),
+                encoding="utf-8",
+            )
+            (temp_root / "^IXIC_report.json").write_text(
+                json.dumps(
+                    build_report(
+                        "^IXIC",
+                        current_price=21850.0,
+                        predicted_price=21960.0,
+                        session_date="2026-04-02",
+                        generated_at="2026-04-02T11:00:00Z",
+                        investment_signal="SELL",
+                        trend_label="STRONG DOWNTREND",
+                        check_engine_light=" RED (Risk Detected - Caution)",
+                        pct_change=-0.5,
+                        direction="downward",
+                    ),
+                    indent=2,
+                ),
+                encoding="utf-8",
+            )
+
+            with patch.object(app_module, "DATA_DIR", temp_root):
+                resp = self.client.get("/api/almanac/iris-snapshot")
+        finally:
+            shutil.rmtree(temp_root, ignore_errors=True)
+
+        self.assertEqual(resp.status_code, 200)
+        data = resp.get_json()
+        self.assertTrue(data["indices"]["spy"]["available"])
+        self.assertTrue(data["indices"]["dji"]["available"])
+        self.assertEqual(data["indices"]["dji"]["session_date"], "2026-04-02")
+        self.assertFalse(data["indices"]["gspc"]["available"])
+        self.assertEqual(data["indices"]["ixic"]["direction"], "downward")
+        self.assertEqual(data["indices"]["ixic"]["investment_signal"], "SELL")
+
+    def test_iris_refresh_returns_lightweight_predictions_for_all_indices(self):
+        app_module.iris_app = FakeIrisApp()
+        app_module._iris_snapshot_cache = {"data": {"stale": True}, "ts": 123.0}
+
+        resp = self.client.get("/api/almanac/iris-refresh")
+
+        self.assertEqual(resp.status_code, 200)
+        data = resp.get_json()
+        self.assertEqual(sorted(data["indices"].keys()), ["dji", "gspc", "ixic", "spy"])
+        self.assertTrue(all(entry["available"] for entry in data["indices"].values()))
+        self.assertEqual(data["indices"]["dji"]["direction"], "upward")
+        self.assertEqual(data["indices"]["gspc"]["investment_signal"], "STRONG BUY")
+        self.assertEqual(data["indices"]["ixic"]["source"], "live_rf_prediction")
+        self.assertEqual(app_module._iris_snapshot_cache["data"], None)
+        self.assertEqual(app_module._iris_snapshot_cache["ts"], 0.0)
+
+    def test_default_tickers_include_spy_and_three_indices(self):
+        for ticker in ("SPY", "^DJI", "^GSPC", "^IXIC"):
+            self.assertIn(ticker, iris_mvp.DEFAULT_TICKERS)
+
+
+if __name__ == "__main__":
+    unittest.main()