""" Chart Generation Functions """ import os from datetime import datetime from zoneinfo import ZoneInfo import pandas as pd import matplotlib matplotlib.use("Agg") # biar bisa jalan tanpa display/GUI (server/headless) import matplotlib.lines as mlines import mplfinance as mpf def generate_chart(ticker: str, df: pd.DataFrame, result: dict, trend_sma_period: int, config: dict = None) -> str | None: """ Bikin chart candlestick PNG (pakai mplfinance) + panel volume, dengan overlay SMA trend, BB lower, Fibonacci levels, dan anotasi titik sinyal (RSI oversold, MACD crossover) - mirip gaya analisa manual TradingView. Args: ticker: Ticker symbol (tanpa .JK suffix) df: DataFrame dengan OHLCV data dan indicators result: Analysis result dict trend_sma_period: Period untuk SMA trend config: Configuration dict Returns: Path ke generated chart file atau None jika gagal """ if config is None: config = {} # Extract configuration values charts_dir = config.get('CHARTS_DIR', 'charts') chart_lookback_candles = config.get('CHART_LOOKBACK_CANDLES', 90) rsi_threshold = config.get('RSI_THRESHOLD', 30) stoch_oversold = config.get('STOCH_OVERSOLD', 20) ma_fast_period = config.get('MA_FAST_PERIOD', 20) ma_slow_period = config.get('MA_SLOW_PERIOD', 50) max_possible_score = config.get('MAX_POSSIBLE_SCORE', 13) market_tz = config.get('MARKET_TZ', 'Asia/Jakarta') # Fibonacci configuration fibonacci_colors = config.get('FIBONACCI_COLORS', ["#ff1744", "#ff9800", "#ffeb3b", "#9e9e9e", "#ffeb3b", "#ff9800", "#ff1744"]) try: os.makedirs(charts_dir, exist_ok=True) plot_df = df.tail(chart_lookback_candles).copy() # Marker: cuma ada nilai di titik sinyal, NaN di titik lain (biar mplfinance # cuma gambar scatter dot di titik itu saja) oversold_marker = plot_df["Close"].where(plot_df["rsi"] < rsi_threshold) macd_cross_mask = (plot_df["macd"].shift(1) <= plot_df["macd_signal"].shift(1)) & \ (plot_df["macd"] > plot_df["macd_signal"]) macd_cross_marker = plot_df["Close"].where(macd_cross_mask) ma_cross_mask = (plot_df["ma_fast"].shift(1) <= plot_df["ma_slow"].shift(1)) & \ (plot_df["ma_fast"] > plot_df["ma_slow"]) ma_cross_marker = plot_df["Close"].where(ma_cross_mask) addplots = [ mpf.make_addplot(plot_df["sma_trend"], color="#888888", linestyle="--", width=1.2), mpf.make_addplot(plot_df["bb_lower"], color="#d62728", linestyle=":", width=0.8), mpf.make_addplot(plot_df["ma_fast"], color="#9467bd", linestyle="-", width=1.0), mpf.make_addplot(plot_df["ma_slow"], color="#8c564b", linestyle="-", width=3.0), mpf.make_addplot(plot_df["vol_sma20"], panel=1, color="#555555", width=1), mpf.make_addplot(plot_df["stoch_k"], panel=2, color="#1f77b4", width=1.1, ylabel="StochRSI"), mpf.make_addplot(plot_df["stoch_d"], panel=2, color="#ff7f0e", width=1.0), ] has_oversold = oversold_marker.notna().any() has_macd_cross = macd_cross_marker.notna().any() has_ma_cross = ma_cross_marker.notna().any() if has_oversold: addplots.append(mpf.make_addplot( oversold_marker, type="scatter", markersize=70, marker="v", color="orange")) if has_macd_cross: addplots.append(mpf.make_addplot( macd_cross_marker, type="scatter", markersize=90, marker="^", color="blue")) if has_ma_cross: addplots.append(mpf.make_addplot( ma_cross_marker, type="scatter", markersize=90, marker="*", color="gold")) # Dark mode style dengan background hitam dan warna candle default style = mpf.make_mpf_style( base_mpf_style="nightclouds", # Gunakan dark theme sebagai base rc={ "font.size": 8, "axes.facecolor": "#1a1a1a", # Background utama hitam "figure.facecolor": "#0d0d0d", # Figure background hitam "axes.edgecolor": "#333333", # Border abu-abu gelap "text.color": "#cccccc", # Text abu-abu terang "ytick.color": "#cccccc", "xtick.color": "#cccccc", "grid.color": "#333333", # Grid abu-abu gelap "grid.linestyle": "--", "grid.linewidth": 0.5, }, marketcolors={ "candle": {"up": "g", "down": "r"}, # Hijau bullish, Merah bearish "edge": {"up": "g", "down": "r"}, # Border candle sesuai warna "wick": {"up": "g", "down": "r"}, # Wick/shadow sesuai warna "ohlc": {"up": "g", "down": "r"}, # OHLC bar sesuai warna "volume": {"up": "g", "down": "r"}, # Volume bar sesuai warna "vcedge": {"up": "g", "down": "r"}, # Volume bar edge sesuai warna "vcdopcod": False, # Volume colors follow candle colors "alpha": 0.9, # Transparency } ) title_reasons = ", ".join(result["reasons"]) ff_text = f"{result['free_float_pct']}%" if result.get("free_float_pct") is not None else "N/A" value_b = result["avg_value_traded"] / 1_000_000_000 info_line = f"Free Float: {ff_text} | Avg Value 20D: Rp {value_b:.2f} Miliar" fig, axes = mpf.plot( plot_df[["Open", "High", "Low", "Close", "Volume"]], type="candle", style=style, volume=True, addplot=addplots, panel_ratios=(3, 1, 1), title=f"\n{ticker} — Score {result['score']}/{max_possible_score}\n{title_reasons}\n{info_line}", figsize=(11, 8.5), returnfig=True, datetime_format="%d %b", xrotation=45, ) # Add Fibonacci horizontal lines if result.get("fibonacci") and result["fibonacci"].get("levels"): fib_ax = axes[0] # Price panel fib_data = result["fibonacci"] # Add horizontal lines untuk tiap Fibonacci level for i, (level_name, level_price) in enumerate(fib_data["levels"].items()): if level_name in ["0.0%", "100.0%"]: color = fibonacci_colors[0] if level_name == "0.0%" else fibonacci_colors[-1] width = 2.0 linestyle = "-" else: # Find index dengan approximate matching (untuk handle floating point precision) level_value = float(level_name.rstrip("%")) / 100 idx = None fib_levels = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1] for j, fib_level in enumerate(fib_levels): if abs(fib_level - level_value) < 0.001: # Tolerance untuk floating point idx = j break if idx is None: idx = i # Fallback ke iteration index color = fibonacci_colors[idx] width = 1.0 # linestyle = "--" linestyle = "-" fib_ax.axhline(y=level_price, color=color, linestyle=linestyle, linewidth=width, alpha=0.7) # Add label untuk level fib_ax.text(0.01, level_price, f" {level_name} ({level_price:,.0f})", transform=fib_ax.get_yaxis_transform(), fontsize=7, verticalalignment='center', color=color, weight='bold') # Garis referensi oversold/overbought di panel Stochastic RSI (panel index 2 -> axes[4] karena # tiap panel punya twin-axis, jadi urutannya: [price, price2, volume, volume2, stoch, stoch2]) try: stoch_ax = axes[4] stoch_ax.axhline(stoch_oversold, color="gray", linestyle=":", linewidth=0.8) stoch_ax.axhline(100 - stoch_oversold, color="gray", linestyle=":", linewidth=0.8) stoch_ax.set_ylim(0, 100) except (IndexError, AttributeError): pass # mplfinance gak auto-legend addplot, jadi bikin manual legend_elems = [ mlines.Line2D([], [], color="#888888", linestyle="--", label=f"SMA{trend_sma_period}"), mlines.Line2D([], [], color="#d62728", linestyle=":", label="BB Lower"), mlines.Line2D([], [], color="#9467bd", label=f"MA{ma_fast_period}"), mlines.Line2D([], [], color="#8c564b", linewidth=3.0, label=f"MA{ma_slow_period}"), ] if has_oversold: legend_elems.append(mlines.Line2D( [], [], color="orange", marker="v", linestyle="None", markersize=8, label="RSI Oversold")) if has_macd_cross: legend_elems.append(mlines.Line2D( [], [], color="blue", marker="^", linestyle="None", markersize=8, label="MACD Cross ↑")) if has_ma_cross: legend_elems.append(mlines.Line2D( [], [], color="gold", marker="*", linestyle="None", markersize=10, label=f"Golden Cross MA{ma_fast_period}/{ma_slow_period}")) axes[0].legend(handles=legend_elems, loc="upper left", fontsize=7.5, ncol=2) # Info harga terakhir + kapan data ini diambil (jam candle terakhir vs jam script jalan) last_candle_time = plot_df.index[-1] last_close = plot_df["Close"].iloc[-1] fetch_time = datetime.now(ZoneInfo(market_tz)) # Check market status - pass as parameter instead of importing to avoid circular dependency # We'll get market status from config or use a default market_open = config.get('market_open', True) market_msg = config.get('market_msg', 'Market buka') # Check apakah data candle terakhir adalah hari ini today = fetch_time.date() last_candle_date = last_candle_time.date() if hasattr(last_candle_time, 'date') else last_candle_time data_stale = last_candle_date < today is_intraday = (plot_df.index.hour != 0).any() or (plot_df.index.minute != 0).any() time_fmt = "%d %b %Y %H:%M" if is_intraday else "%d %b %Y" # Market status indicator market_status_text = "[MARKET BUKA]" if market_open else "[MARKET TUTUP]" if data_stale and not market_open: market_status_text += f" (Data: {last_candle_time.strftime(time_fmt)})" # Fibonacci info fib_info = "" if result.get("fibonacci") and result["fibonacci"].get("levels"): fib = result["fibonacci"] current_close = result["close"] # Use result close price # Cek posisi harga relatif terhadap Fibonacci levels fib_levels = fib["levels"] near_level = None min_dist = float('inf') for level_name, level_price in fib_levels.items(): dist = abs(current_close - level_price) / current_close * 100 if dist < min_dist: min_dist = dist near_level = level_name if near_level and min_dist < 5: # Kalau dalam 5% dari suatu level fib_info = f"\nNear Fibo {near_level} ({min_dist:.1f}% away)" info_text = ( f"{market_status_text}{fib_info}\n" f"Harga Terakhir: {last_close:,.0f}\n" f"Candle Terakhir: {last_candle_time.strftime(time_fmt)}\n" f"Analisa: {fetch_time.strftime('%d %b %Y %H:%M')} WIB" ) axes[0].text( 0.99, 0.98, info_text, transform=axes[0].transAxes, fontsize=8, ha="right", va="top", bbox=dict(boxstyle="round", facecolor="#0d0d0d", alpha=0.85, edgecolor="#333333", pad=5) ) filename = f"{ticker}_{datetime.now().strftime('%Y%m%d_%H%M')}.png" filepath = os.path.join(charts_dir, filename) fig.savefig(filepath, dpi=130, bbox_inches="tight") import matplotlib.pyplot as plt plt.close(fig) return filepath except Exception as e: print(f"[WARN] Gagal bikin chart {ticker}: {e}") return None