Add chart generation: /chart command + /signal sends chart

- charts.py: candlestick (mplfinance, dark theme) with EMA20/50
  overlay, Bollinger Bands, RSI subplot and 30/70 reference lines
- /signal now sends chart image with analysis as caption
- New /chart <PAIR> <TF> command for standalone chart view

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

charts.py

@@ -0,0 +1,121 @@
+"""Chart generation — produces a PNG bytes object suitable for Telegram reply_photo."""
+from __future__ import annotations
+
+import io
+
+import matplotlib
+matplotlib.use("Agg")  # non-interactive backend — must be set before other imports
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import mplfinance as mpf
+import pandas as pd
+
+
+_STYLE = mpf.make_mpf_style(
+    base_mpf_style="nightclouds",
+    rc={
+        "font.size": 9,
+        "axes.titlesize": 11,
+        "axes.labelsize": 8,
+    },
+    facecolor="#1a1a2e",
+    figcolor="#1a1a2e",
+    gridcolor="#2a2a3e",
+)
+
+
+def _rsi_series(close: pd.Series, period: int = 14) -> pd.Series:
+    delta = close.diff()
+    gain = delta.clip(lower=0.0)
+    loss = -delta.clip(upper=0.0)
+    avg_gain = gain.ewm(alpha=1.0 / period, adjust=False).mean()
+    avg_loss = loss.ewm(alpha=1.0 / period, adjust=False).mean()
+    rs = avg_gain / avg_loss.replace(0.0, pd.NA)
+    return (100.0 - (100.0 / (1.0 + rs))).fillna(100.0)
+
+
+def generate(
+    df: pd.DataFrame,
+    pair: str,
+    tf: str,
+    verdict: str = "",
+    confidence: int = 0,
+    candles: int = 80,
+) -> bytes:
+    """Render a candlestick chart with EMA/BB overlays and RSI panel.
+
+    Returns raw PNG bytes for direct use with Telegram reply_photo.
+    """
+    # Compute indicators on full df (warmup), then slice for display
+    close = df["close"]
+    ema20_full = close.ewm(span=20, adjust=False).mean()
+    ema50_full = close.ewm(span=50, adjust=False).mean()
+    mid_full = close.rolling(20).mean()
+    std_full = close.rolling(20).std(ddof=0)
+    bb_upper_full = mid_full + 2.0 * std_full
+    bb_lower_full = mid_full - 2.0 * std_full
+    rsi_full = _rsi_series(close)
+
+    n = min(candles, len(df))
+    data = df.tail(n).copy()
+    data.columns = [c.capitalize() for c in data.columns]  # mplfinance expects Open/High/Low/Close
+
+    ema20 = ema20_full.tail(n).values
+    ema50 = ema50_full.tail(n).values
+    bb_upper = bb_upper_full.tail(n).values
+    bb_lower = bb_lower_full.tail(n).values
+    rsi_vals = rsi_full.tail(n).values
+
+    addplots = [
+        mpf.make_addplot(ema20, color="#00bfff", width=1.4),
+        mpf.make_addplot(ema50, color="#ffa500", width=1.4),
+        mpf.make_addplot(bb_upper, color="#888888", width=0.8, linestyle="--", alpha=0.7),
+        mpf.make_addplot(bb_lower, color="#888888", width=0.8, linestyle="--", alpha=0.7),
+        mpf.make_addplot(rsi_vals, panel=1, color="#b388ff", width=1.2, ylabel="RSI"),
+    ]
+
+    verdict_label = f"  {verdict} {confidence}%" if verdict else ""
+    title = f"{pair}  {tf}{verdict_label}"
+
+    fig, axes = mpf.plot(
+        data,
+        type="candle",
+        style=_STYLE,
+        addplot=addplots,
+        volume=False,
+        panel_ratios=(3, 1),
+        title=title,
+        returnfig=True,
+        figsize=(11, 6.5),
+        tight_layout=False,
+    )
+
+    # axes: [price_ax, price_twin, rsi_ax, ...]
+    rsi_ax = axes[2]
+    rsi_ax.axhline(70, color="#ff5252", linewidth=0.8, linestyle="--", alpha=0.8)
+    rsi_ax.axhline(50, color="#777777", linewidth=0.5, linestyle="--", alpha=0.5)
+    rsi_ax.axhline(30, color="#69f0ae", linewidth=0.8, linestyle="--", alpha=0.8)
+    rsi_ax.set_ylim(0, 100)
+    rsi_ax.yaxis.set_major_locator(ticker.MultipleLocator(20))
+
+    # EMA legend on price panel
+    price_ax = axes[0]
+    price_ax.plot([], [], color="#00bfff", linewidth=1.4, label="EMA 20")
+    price_ax.plot([], [], color="#ffa500", linewidth=1.4, label="EMA 50")
+    price_ax.plot([], [], color="#888888", linewidth=0.8, linestyle="--", label="BB(20,2)")
+    price_ax.legend(
+        loc="upper left",
+        fontsize=7,
+        facecolor="#1a1a2e",
+        edgecolor="#444466",
+        labelcolor="white",
+    )
+
+    fig.subplots_adjust(left=0.08, right=0.97, top=0.92, bottom=0.10, hspace=0.05)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=130, facecolor=fig.get_facecolor())
+    plt.close(fig)
+    buf.seek(0)
+    return buf.read()

main.py

@@ -11,6 +11,7 @@ from telegram.ext import (
 )
 
 from config import SUPPORTED_PAIRS, SUPPORTED_TIMEFRAMES, TELEGRAM_TOKEN
+from charts import generate as generate_chart
 from indicators import bollinger, macd, rsi
 from signals import HTF_OF, analyze, emoji
 from twelvedata import fetch_ohlc
@@ -31,7 +32,8 @@ WELCOME = (
     "/rsi <PAIR> <TF> — RSI value\n"
     "/macd <PAIR> <TF> — MACD line/signal/histogram\n"
     "/bb <PAIR> <TF> — Bollinger Bands\n"
-    "/signal <PAIR> <TF> — multi-indicator weighted verdict with HTF trend filter\n"
+    "/signal <PAIR> <TF> — multi-indicator weighted verdict with chart\n"
+    "/chart <PAIR> <TF> — candlestick chart with EMA/BB/RSI\n"
     "/help — this message"
 ) + DISCLAIMER
 
@@ -131,6 +133,18 @@ def _format_reading(r) -> str:
     return f"• {r.name}: {r.value} ({sign}{r.contribution})"
 
 
+async def chart_cmd(update: Update, ctx: ContextTypes.DEFAULT_TYPE) -> None:
+    try:
+        pair, tf = _parse_pair_tf(ctx.args)
+        df = await fetch_ohlc(pair, tf)
+        img = generate_chart(df, pair, tf)
+        caption = f"*{pair}* {tf} — candlestick · EMA20/50 · BB(20,2) · RSI(14)" + DISCLAIMER
+        await update.message.reply_photo(img, caption=caption, parse_mode="Markdown")
+    except Exception as e:
+        logger.exception("chart_cmd failed")
+        await _reply(update, f"Error: {e}")
+
+
 async def signal_cmd(update: Update, ctx: ContextTypes.DEFAULT_TYPE) -> None:
     try:
         pair, tf = _parse_pair_tf(ctx.args)
@@ -177,7 +191,13 @@ async def signal_cmd(update: Update, ctx: ContextTypes.DEFAULT_TYPE) -> None:
         if report["notes"]:
             lines += ["", *[f"_{n}_" for n in report["notes"]]]
 
-        await _reply(update, "\n".join(lines))
+        caption = "\n".join(lines) + DISCLAIMER
+        img = generate_chart(
+            df, pair, tf,
+            verdict=report["verdict"],
+            confidence=report["confidence"],
+        )
+        await update.message.reply_photo(img, caption=caption, parse_mode="Markdown")
     except Exception as e:
         logger.exception("signal_cmd failed")
         await _reply(update, f"Error: {e}")
@@ -191,7 +211,8 @@ BOT_COMMANDS: list[BotCommand] = [
     BotCommand("rsi", "RSI(14) — /rsi EURUSD 1h"),
     BotCommand("macd", "MACD(12,26,9) — /macd EURUSD 1h"),
     BotCommand("bb", "Bollinger Bands(20,2) — /bb EURUSD 1h"),
-    BotCommand("signal", "Multi-indicator verdict — /signal EURUSD 1h"),
+    BotCommand("signal", "Multi-indicator verdict + chart — /signal EURUSD 1h"),
+    BotCommand("chart", "Candlestick chart with EMA/BB/RSI — /chart EURUSD 1h"),
 ]
 
 
@@ -216,6 +237,7 @@ def main() -> None:
     app.add_handler(CommandHandler("macd", macd_cmd))
     app.add_handler(CommandHandler("bb", bb_cmd))
     app.add_handler(CommandHandler("signal", signal_cmd))
+    app.add_handler(CommandHandler("chart", chart_cmd))
     logger.info("Bot starting...")
     app.run_polling()
 

requirements.txt

@@ -2,3 +2,5 @@ python-telegram-bot>=21.6
 httpx>=0.27.2
 pandas>=2.2.3
 python-dotenv>=1.0.1
+mplfinance>=0.12.10b0
+matplotlib>=3.9.0