Harwinder
de96af798c
- 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>
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
"""Chart generation — produces a PNG bytes object suitable for Telegram reply_photo."""
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from __future__ import annotations
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import io
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import matplotlib
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matplotlib.use("Agg") # non-interactive backend — must be set before other imports
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import mplfinance as mpf
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import pandas as pd
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_STYLE = mpf.make_mpf_style(
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base_mpf_style="nightclouds",
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rc={
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"font.size": 9,
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"axes.titlesize": 11,
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"axes.labelsize": 8,
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},
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facecolor="#1a1a2e",
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figcolor="#1a1a2e",
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gridcolor="#2a2a3e",
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)
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def _rsi_series(close: pd.Series, period: int = 14) -> pd.Series:
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delta = close.diff()
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gain = delta.clip(lower=0.0)
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loss = -delta.clip(upper=0.0)
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avg_gain = gain.ewm(alpha=1.0 / period, adjust=False).mean()
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avg_loss = loss.ewm(alpha=1.0 / period, adjust=False).mean()
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rs = avg_gain / avg_loss.replace(0.0, pd.NA)
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return (100.0 - (100.0 / (1.0 + rs))).fillna(100.0)
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def generate(
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df: pd.DataFrame,
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pair: str,
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tf: str,
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verdict: str = "",
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confidence: int = 0,
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candles: int = 80,
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) -> bytes:
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"""Render a candlestick chart with EMA/BB overlays and RSI panel.
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Returns raw PNG bytes for direct use with Telegram reply_photo.
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"""
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# Compute indicators on full df (warmup), then slice for display
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close = df["close"]
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ema20_full = close.ewm(span=20, adjust=False).mean()
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ema50_full = close.ewm(span=50, adjust=False).mean()
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mid_full = close.rolling(20).mean()
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std_full = close.rolling(20).std(ddof=0)
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bb_upper_full = mid_full + 2.0 * std_full
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bb_lower_full = mid_full - 2.0 * std_full
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rsi_full = _rsi_series(close)
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n = min(candles, len(df))
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data = df.tail(n).copy()
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data.columns = [c.capitalize() for c in data.columns] # mplfinance expects Open/High/Low/Close
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ema20 = ema20_full.tail(n).values
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ema50 = ema50_full.tail(n).values
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bb_upper = bb_upper_full.tail(n).values
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bb_lower = bb_lower_full.tail(n).values
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rsi_vals = rsi_full.tail(n).values
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addplots = [
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mpf.make_addplot(ema20, color="#00bfff", width=1.4),
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mpf.make_addplot(ema50, color="#ffa500", width=1.4),
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mpf.make_addplot(bb_upper, color="#888888", width=0.8, linestyle="--", alpha=0.7),
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mpf.make_addplot(bb_lower, color="#888888", width=0.8, linestyle="--", alpha=0.7),
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mpf.make_addplot(rsi_vals, panel=1, color="#b388ff", width=1.2, ylabel="RSI"),
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]
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verdict_label = f" {verdict} {confidence}%" if verdict else ""
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title = f"{pair} {tf}{verdict_label}"
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fig, axes = mpf.plot(
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data,
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type="candle",
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style=_STYLE,
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addplot=addplots,
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volume=False,
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panel_ratios=(3, 1),
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title=title,
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returnfig=True,
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figsize=(11, 6.5),
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tight_layout=False,
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)
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# axes: [price_ax, price_twin, rsi_ax, ...]
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rsi_ax = axes[2]
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rsi_ax.axhline(70, color="#ff5252", linewidth=0.8, linestyle="--", alpha=0.8)
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rsi_ax.axhline(50, color="#777777", linewidth=0.5, linestyle="--", alpha=0.5)
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rsi_ax.axhline(30, color="#69f0ae", linewidth=0.8, linestyle="--", alpha=0.8)
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rsi_ax.set_ylim(0, 100)
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rsi_ax.yaxis.set_major_locator(ticker.MultipleLocator(20))
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# EMA legend on price panel
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price_ax = axes[0]
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price_ax.plot([], [], color="#00bfff", linewidth=1.4, label="EMA 20")
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price_ax.plot([], [], color="#ffa500", linewidth=1.4, label="EMA 50")
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price_ax.plot([], [], color="#888888", linewidth=0.8, linestyle="--", label="BB(20,2)")
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price_ax.legend(
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loc="upper left",
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fontsize=7,
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facecolor="#1a1a2e",
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edgecolor="#444466",
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labelcolor="white",
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)
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fig.subplots_adjust(left=0.08, right=0.97, top=0.92, bottom=0.10, hspace=0.05)
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buf = io.BytesIO()
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fig.savefig(buf, format="png", dpi=130, facecolor=fig.get_facecolor())
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plt.close(fig)
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buf.seek(0)
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return buf.read()
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