Add pivot point support/resistance chart to dashboard

Add Classic Pivot Points (P, R1-R3, S1-S3) computed from previous day's
OHLC data, displayed as horizontal lines on an interactive Plotly.js
candlestick chart at /chart with instrument/granularity selectors.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/dashboard.py

@@ -13,7 +13,8 @@ from pathlib import Path
 from functools import wraps
 
 import yaml
-from flask import Flask, render_template, request, redirect, url_for, flash, Response
+import pandas as pd
+from flask import Flask, render_template, request, redirect, url_for, flash, Response, jsonify
 
 app = Flask(__name__, template_folder=str(Path(__file__).resolve().parent.parent / "templates"))
 app.secret_key = os.urandom(24)
@@ -289,6 +290,86 @@ def killswitch():
     return redirect(url_for("index"))
 
 
+@app.route("/chart")
+@requires_auth
+def chart():
+    """Candlestick chart with Classic Pivot Point support/resistance levels."""
+    instrument = request.args.get("instrument", "EUR_USD")
+    granularity = request.args.get("granularity", "M15")
+
+    # Validate inputs
+    if instrument not in VALID_INSTRUMENTS:
+        instrument = "EUR_USD"
+    if granularity not in VALID_GRANULARITIES:
+        granularity = "M15"
+
+    ohlc_json = "[]"
+    pivot_json = "{}"
+    error_msg = None
+
+    try:
+        from supabase import create_client
+        from dotenv import load_dotenv
+
+        env_path = APP_ROOT / "config" / ".env"
+        if env_path.exists():
+            load_dotenv(env_path)
+
+        url = os.environ.get("SUPABASE_URL")
+        key = os.environ.get("SUPABASE_KEY")
+
+        if url and key:
+            sb = create_client(url, key)
+            cfg = load_config()
+            table = cfg.get("supabase", {}).get("table", "fx_candles")
+
+            resp = (
+                sb.table(table)
+                .select("time,open,high,low,close")
+                .eq("instrument", instrument)
+                .eq("granularity", granularity)
+                .order("time", desc=True)
+                .limit(500)
+                .execute()
+            )
+
+            rows = resp.data if resp.data else []
+            if rows:
+                df = pd.DataFrame(rows)
+                df["time"] = pd.to_datetime(df["time"])
+                df = df.sort_values("time").set_index("time")
+                for col in ["open", "high", "low", "close"]:
+                    df[col] = df[col].astype(float)
+
+                # Compute pivot points
+                from data_engine import add_pivot_points
+                df = add_pivot_points(df)
+
+                # Prepare OHLC JSON
+                ohlc_data = df[["open", "high", "low", "close"]].reset_index()
+                ohlc_data["time"] = ohlc_data["time"].dt.strftime("%Y-%m-%d %H:%M")
+                ohlc_json = ohlc_data.to_json(orient="records")
+
+                # Prepare pivot levels (latest non-null values)
+                pivot_cols = ["pivot", "r1", "r2", "r3", "s1", "s2", "s3"]
+                latest = df[pivot_cols].dropna().iloc[-1] if df[pivot_cols].dropna().shape[0] > 0 else None
+                if latest is not None:
+                    pivot_json = latest.to_json()
+        else:
+            error_msg = "Supabase credentials not configured."
+    except Exception as e:
+        error_msg = f"Could not load candle data: {e}"
+
+    return render_template("chart.html",
+                           ohlc_json=ohlc_json,
+                           pivot_json=pivot_json,
+                           instrument=instrument,
+                           granularity=granularity,
+                           instruments=VALID_INSTRUMENTS,
+                           granularities=VALID_GRANULARITIES,
+                           error_msg=error_msg)
+
+
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------

src/data_engine.py

@@ -90,6 +90,46 @@ def add_vwap(df, period=20):
     return df
 
 
+def add_pivot_points(df):
+    """
+    Add Classic Pivot Point support/resistance levels.
+
+    Resamples intraday OHLC data to daily bars, computes pivot levels from the
+    *previous* day's High/Low/Close, and merges them back onto the original
+    DataFrame so every intraday bar carries the current day's pivot levels.
+
+    Columns added: pivot, r1, r2, r3, s1, s2, s3
+    """
+    # Need a DatetimeIndex to resample
+    df2 = df.copy()
+    daily = df2.resample("D").agg({"high": "max", "low": "min", "close": "last"}).dropna()
+
+    # Compute pivots from previous day
+    daily["pivot"] = (daily["high"] + daily["low"] + daily["close"]) / 3
+    daily["r1"] = 2 * daily["pivot"] - daily["low"]
+    daily["s1"] = 2 * daily["pivot"] - daily["high"]
+    daily["r2"] = daily["pivot"] + (daily["high"] - daily["low"])
+    daily["s2"] = daily["pivot"] - (daily["high"] - daily["low"])
+    daily["r3"] = daily["high"] + 2 * (daily["pivot"] - daily["low"])
+    daily["s3"] = daily["low"] - 2 * (daily["high"] - daily["pivot"])
+
+    # Shift so today's bars use *yesterday's* pivot levels
+    pivot_cols = ["pivot", "r1", "r2", "r3", "s1", "s2", "s3"]
+    daily_pivots = daily[pivot_cols].shift(1)
+
+    # Assign a date key to the original df for merging
+    df["_date"] = df.index.date
+    daily_pivots["_date"] = daily_pivots.index.date
+
+    df = df.merge(daily_pivots, on="_date", how="left")
+    df.drop(columns=["_date"], inplace=True)
+
+    # Restore the original DatetimeIndex
+    df.index = df2.index
+
+    return df
+
+
 def build_all_features(df, config=None):
     """
     Run a standard set of features. config is optional dict matching the