Enhance exchange configurability and indicator calculations

dashboard/app.py

@@ -6,17 +6,29 @@
 from plotly.subplots import make_subplots
 import streamlit as st
 
-from quant_core import TZ, add_indicators, classify_signal, latest_snapshot
+from quant_core import TZ, add_indicators, classify_signal, latest_snapshot, load_settings
 
 st.set_page_config(page_title="BTC Quant Dashboard", layout="wide")
 
 # ---------- Sidebar Controls ----------
 st.sidebar.title("Controls")
-timeframe = st.sidebar.selectbox("Timeframe", ["5m", "15m", "1h", "4h", "1d"], index=2)
+SETTINGS = load_settings()
+exchange_id = SETTINGS.get("exchange", "kraken")
+symbol = SETTINGS.get("symbol", "BTC/USD")
+available_timeframes = ["5m", "15m", "1h", "4h", "1d"]
+default_timeframe = SETTINGS.get("timeframe", "1h")
+try:
+    default_index = available_timeframes.index(default_timeframe)
+except ValueError:
+    default_index = 2
+
+timeframe = st.sidebar.selectbox("Timeframe", available_timeframes, index=default_index)
 refresh_s = st.sidebar.slider("Refresh (seconds)", 5, 120, 20, step=5)
 show_ema = st.sidebar.checkbox("Show EMA(50/200)", value=True)
 show_sma = st.sidebar.checkbox("Show SMA(50/200)", value=True)
-st.sidebar.caption("Data source: Kraken via ccxt. Times in America/Denver.")
+st.sidebar.caption(
+    f"Data source: {exchange_id.title()} via ccxt. Times in {TZ.zone}."
+)
 
 # ---------- Header ----------
 st.markdown(
@@ -46,9 +58,15 @@
     st.session_state["snapshot_timeframe"] = timeframe
 
 error_message = None
+limit = SETTINGS.get("limit", 500)
 
 try:
-    snapshot = latest_snapshot(timeframe)
+    snapshot = latest_snapshot(
+        timeframe=timeframe,
+        limit=limit,
+        exchange_id=exchange_id,
+        symbol=symbol,
+    )
     st.session_state["snapshot"] = snapshot
     st.session_state["snapshot_cached_at"] = datetime.now(TZ)
 except Exception as exc:  # noqa: BLE001 - we want to show any failure to the user

quant_core.py

@@ -1,89 +1,158 @@
-# Shared core logic used by both Streamlit and Colab
+"""Core quantitative utilities shared by the CLI and dashboard applications."""
+
+from __future__ import annotations
+
 import argparse
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, Tuple
+
 import ccxt
 import pandas as pd
 import pytz
 import yaml
-from datetime import datetime
-from pathlib import Path
 
-TZ = pytz.timezone('America/Denver')
+TZ = pytz.timezone("America/Denver")
+
 
+def load_settings(path: str | Path | None = None) -> Dict[str, Any]:
+    """Load YAML settings from ``conf/settings.yml``.
+
+    Parameters
+    ----------
+    path:
+        Optional override for the settings file location.
+    """
 
-def load_settings(path: str | None = None):
-    """Load YAML settings from conf/settings.yml."""
     if path is None:
         path = Path(__file__).resolve().parent / "conf" / "settings.yml"
+    else:
+        path = Path(path)
+
     with open(path, "r", encoding="utf-8") as fh:
         return yaml.safe_load(fh)
 
 
-def _exchange():
-    ex = ccxt.kraken({'enableRateLimit': True})
-    ex.load_markets()
-    market = 'BTC/USD' if 'BTC/USD' in ex.symbols else 'XBT/USD'
-    return ex, market
-
-
-def fetch_ohlcv(timeframe='1h', limit=500):
-    ex, market = _exchange()
-    ohlcv = ex.fetch_ohlcv(market, timeframe=timeframe, limit=limit)
-    df = pd.DataFrame(ohlcv, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
-    df['time'] = pd.to_datetime(df['time'], unit='ms', utc=True).dt.tz_convert(TZ)
-    df.set_index('time', inplace=True)
+def _get_exchange_and_symbol(exchange_id: str, symbol: str) -> Tuple[ccxt.Exchange, str]:
+    """Instantiate an exchange client and validate the requested symbol."""
+
+    try:
+        exchange_cls = getattr(ccxt, exchange_id.lower())
+    except AttributeError as exc:  # pragma: no cover - defensive guard
+        raise ValueError(f"Exchange '{exchange_id}' is not supported by ccxt.") from exc
+
+    exchange = exchange_cls({"enableRateLimit": True})
+    exchange.load_markets()
+
+    if symbol not in exchange.symbols:
+        base, _, quote = symbol.partition("/")
+        # Allow common BTC base symbol aliases when a direct match is missing.
+        btc_aliases = {
+            "BTC": ["XBT"],
+            "XBT": ["BTC"],
+        }
+        for alt_base in btc_aliases.get(base.upper(), []):
+            candidate = f"{alt_base}/{quote}" if quote else alt_base
+            if candidate in exchange.symbols:
+                symbol = candidate
+                break
+        else:
+            raise ValueError(
+                f"Symbol '{symbol}' is not available on exchange '{exchange_id}'."
+            )
+
+    return exchange, symbol
+
+
+def fetch_ohlcv(
+    timeframe: str = "1h",
+    limit: int = 500,
+    *,
+    exchange_id: str = "kraken",
+    symbol: str = "BTC/USD",
+) -> pd.DataFrame:
+    """Fetch OHLCV data and return it as a timezone-aware dataframe."""
+
+    exchange, resolved_symbol = _get_exchange_and_symbol(exchange_id, symbol)
+    ohlcv = exchange.fetch_ohlcv(resolved_symbol, timeframe=timeframe, limit=limit)
+    df = pd.DataFrame(ohlcv, columns=["time", "open", "high", "low", "close", "volume"])
+    df["time"] = pd.to_datetime(df["time"], unit="ms", utc=True).dt.tz_convert(TZ)
+    df.set_index("time", inplace=True)
     return df
 
 
-def add_indicators(df):
+def add_indicators(df: pd.DataFrame) -> pd.DataFrame:
+    """Append common trend and momentum indicators to *df*."""
+
     df = df.copy()
 
     # SMAs
-    df['SMA50'] = df['close'].rolling(50).mean()
-    df['SMA200'] = df['close'].rolling(200).mean()
+    df["SMA50"] = df["close"].rolling(50).mean()
+    df["SMA200"] = df["close"].rolling(200).mean()
 
     # EMAs
-    df['EMA50'] = df['close'].ewm(span=50, adjust=False).mean()
-    df['EMA200'] = df['close'].ewm(span=200, adjust=False).mean()
-
-    # RSI (14)
-    delta = df['close'].diff()
-    gain = delta.clip(lower=0).rolling(14).mean()
-    loss = (-delta.clip(upper=0)).rolling(14).mean()
-    rs = gain / loss
-    df['RSI'] = 100 - (100 / (1 + rs))
+    df["EMA50"] = df["close"].ewm(span=50, adjust=False).mean()
+    df["EMA200"] = df["close"].ewm(span=200, adjust=False).mean()
+
+    # RSI (Wilder's smoothing, period 14)
+    delta = df["close"].diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    avg_gain = gain.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
+    avg_loss = loss.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+    rsi = rsi.where(avg_loss != 0, 100)
+    rsi = rsi.where(avg_gain != 0, 0)
+    df["RSI"] = rsi
 
     # MACD (12,26,9)
-    ema12 = df['close'].ewm(span=12, adjust=False).mean()
-    ema26 = df['close'].ewm(span=26, adjust=False).mean()
-    df['MACD'] = ema12 - ema26
-    df['MACDSignal'] = df['MACD'].ewm(span=9, adjust=False).mean()
-    df['MACDHist'] = df['MACD'] - df['MACDSignal']
+    ema12 = df["close"].ewm(span=12, adjust=False).mean()
+    ema26 = df["close"].ewm(span=26, adjust=False).mean()
+    df["MACD"] = ema12 - ema26
+    df["MACDSignal"] = df["MACD"].ewm(span=9, adjust=False).mean()
+    df["MACDHist"] = df["MACD"] - df["MACDSignal"]
 
     return df
 
 
-def classify_signal(row):
-    sma_bull = row['SMA50'] > row['SMA200']
-    sma_bear = row['SMA50'] < row['SMA200']
-    ema_bull = row['EMA50'] > row['EMA200']
-    ema_bear = row['EMA50'] < row['EMA200']
-    rsi_bull = row['RSI'] >= 50
-    rsi_bear = row['RSI'] <= 50
-    macd_bull = row['MACD'] > row['MACDSignal']
-    macd_bear = row['MACD'] < row['MACDSignal']
+def classify_signal(row: pd.Series) -> str:
+    """Return a coarse market regime classification for the latest row."""
+
+    sma_bull = row["SMA50"] > row["SMA200"]
+    sma_bear = row["SMA50"] < row["SMA200"]
+    ema_bull = row["EMA50"] > row["EMA200"]
+    ema_bear = row["EMA50"] < row["EMA200"]
+    rsi_bull = row["RSI"] > 50
+    rsi_bear = row["RSI"] < 50
+    macd_bull = row["MACD"] > row["MACDSignal"]
+    macd_bear = row["MACD"] < row["MACDSignal"]
 
     bull_votes = sum([sma_bull, ema_bull, rsi_bull, macd_bull])
     bear_votes = sum([sma_bear, ema_bear, rsi_bear, macd_bear])
 
     if bull_votes >= 3 and bull_votes > bear_votes:
-        return 'Bullish'
+        return "Bullish"
     if bear_votes >= 3 and bear_votes > bull_votes:
-        return 'Bearish'
-    return 'Neutral'
-
-
-def latest_snapshot(timeframe='1h', limit=500):
-    df = add_indicators(fetch_ohlcv(timeframe, limit=limit))
+        return "Bearish"
+    return "Neutral"
+
+
+def latest_snapshot(
+    timeframe: str = "1h",
+    limit: int = 500,
+    *,
+    exchange_id: str = "kraken",
+    symbol: str = "BTC/USD",
+) -> tuple[pd.DataFrame, str, str]:
+    df = add_indicators(
+        fetch_ohlcv(
+            timeframe=timeframe,
+            limit=limit,
+            exchange_id=exchange_id,
+            symbol=symbol,
+        )
+    )
     last = df.iloc[-1]
     sig = classify_signal(last)
     now = datetime.now(TZ).strftime('%Y-%m-%d %H:%M:%S')
@@ -110,6 +179,8 @@ def main():
         return
 
     settings = load_settings()
+    exchange_id = settings.get("exchange", "kraken")
+    symbol = settings.get("symbol", "BTC/USD")
 
     if args.report is not None:
         base = args.report or settings.get("logging", {}).get("dir", "logs")
@@ -123,7 +194,14 @@ def main():
     limit = settings.get("limit", 500)
 
     if args.live:
-        df = add_indicators(fetch_ohlcv(timeframe=timeframe, limit=limit))
+        df = add_indicators(
+            fetch_ohlcv(
+                timeframe=timeframe,
+                limit=limit,
+                exchange_id=exchange_id,
+                symbol=symbol,
+            )
+        )
         sig = classify_signal(df.iloc[-1])
         print(sig)
         return
@@ -133,7 +211,12 @@ def main():
         return
 
     # Default behavior
-    df, sig, now = latest_snapshot(timeframe=timeframe, limit=limit)
+    df, sig, now = latest_snapshot(
+        timeframe=timeframe,
+        limit=limit,
+        exchange_id=exchange_id,
+        symbol=symbol,
+    )
     print(df.tail())
     print(f"Signal: {sig} at {now}")
 

requirements.txt

@@ -1,5 +1,6 @@
 ccxt
 pandas
 pytz
+PyYAML
 plotly
 streamlit