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TradingView-Style Chart Image Generator
Generates realistic candlestick charts with indicators.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.patches import FancyBboxPatch
import matplotlib.patches as mpatches
import numpy as np
import pandas as pd
from typing import Optional, List, Dict, Any
import os
# TradingView color scheme
TV_BG = "#0e1117"
TV_FG = "#d1d4dc"
TV_GRID = "#1e222d"
TV_RED = "#ef5350"
TV_GREEN = "#26a69a"
TV_BLUE = "#2962ff"
TV_ORANGE = "#ff9800"
TV_PURPLE = "#ab47bc"
TV_YELLOW = "#ffeb3b"
TV_CYAN = "#00bcd4"
def generate_chart_image(
df: pd.DataFrame,
symbol: str,
timeframe: str,
output_path: str,
indicators: Optional[Dict[str, Any]] = None,
show_volume: bool = True,
show_sma: Optional[List[int]] = None,
show_ema: Optional[List[int]] = None,
show_bollinger: bool = False,
show_vwap: bool = False,
show_support_resistance: bool = False,
support_levels: Optional[List[float]] = None,
resistance_levels: Optional[List[float]] = None,
show_pattern_annotation: bool = False,
pattern_name: Optional[str] = None,
figsize: tuple = (12, 8),
dpi: int = 150,
) -> str:
"""
Generate a TradingView-style chart image.
Returns the output file path.
"""
# Determine subplot layout
n_subplots = 1 # Main price chart
if show_volume:
n_subplots += 1
if indicators and "rsi" in indicators:
n_subplots += 1
if indicators and "macd" in indicators:
n_subplots += 1
# Height ratios
height_ratios = [3]
if show_volume:
height_ratios.append(0.8)
if indicators and "rsi" in indicators:
height_ratios.append(1.0)
if indicators and "macd" in indicators:
height_ratios.append(1.0)
fig, axes = plt.subplots(
n_subplots, 1,
figsize=figsize,
gridspec_kw={"height_ratios": height_ratios},
sharex=True,
)
if n_subplots == 1:
axes = [axes]
fig.patch.set_facecolor(TV_BG)
for ax in axes:
ax.set_facecolor(TV_BG)
ax.tick_params(colors=TV_FG, which="both")
for spine in ax.spines.values():
spine.set_color(TV_GRID)
ax.grid(True, alpha=0.15, color=TV_GRID)
ax_main = axes[0]
date_nums = mdates.date2num(df.index)
# Plot candlesticks
up = df["close"] >= df["open"]
down = ~up
width = 0.6
width2 = 0.1
# Green candles (up)
ax_main.bar(
date_nums[up], df["close"][up] - df["open"][up],
width, bottom=df["open"][up],
color=TV_GREEN, edgecolor=TV_GREEN, linewidth=0.5,
)
ax_main.bar(
date_nums[up], df["high"][up] - df["close"][up],
width2, bottom=df["close"][up],
color=TV_GREEN, edgecolor=TV_GREEN,
)
ax_main.bar(
date_nums[up], df["open"][up] - df["low"][up],
width2, bottom=df["low"][up],
color=TV_GREEN, edgecolor=TV_GREEN,
)
# Red candles (down)
ax_main.bar(
date_nums[down], df["open"][down] - df["close"][down],
width, bottom=df["close"][down],
color=TV_RED, edgecolor=TV_RED, linewidth=0.5,
)
ax_main.bar(
date_nums[down], df["high"][down] - df["open"][down],
width2, bottom=df["open"][down],
color=TV_RED, edgecolor=TV_RED,
)
ax_main.bar(
date_nums[down], df["close"][down] - df["low"][down],
width2, bottom=df["low"][down],
color=TV_RED, edgecolor=TV_RED,
)
# Plot moving averages
if show_sma:
colors = [TV_BLUE, TV_ORANGE, TV_PURPLE, TV_CYAN]
for i, period in enumerate(show_sma):
col = f"sma_{period}"
if col in df.columns:
ax_main.plot(
date_nums, df[col],
color=colors[i % len(colors)],
linewidth=0.8, alpha=0.8,
label=f"SMA {period}",
)
if show_ema:
colors = [TV_YELLOW, TV_CYAN, TV_ORANGE, TV_BLUE]
for i, period in enumerate(show_ema):
col = f"ema_{period}"
if col in df.columns:
ax_main.plot(
date_nums, df[col],
color=colors[i % len(colors)],
linewidth=0.8, alpha=0.8, linestyle="--",
label=f"EMA {period}",
)
# Bollinger Bands
if show_bollinger and "bb_upper" in df.columns:
ax_main.plot(date_nums, df["bb_upper"], color=TV_BLUE, linewidth=0.5, alpha=0.5)
ax_main.plot(date_nums, df["bb_lower"], color=TV_BLUE, linewidth=0.5, alpha=0.5)
ax_main.fill_between(
date_nums, df["bb_upper"], df["bb_lower"],
color=TV_BLUE, alpha=0.05,
)
# VWAP
if show_vwap and "vwap" in df.columns:
ax_main.plot(
date_nums, df["vwap"],
color=TV_PURPLE, linewidth=0.8, alpha=0.7, linestyle=":",
label="VWAP",
)
# Support/Resistance
if show_support_resistance:
if support_levels:
for level in support_levels:
ax_main.axhline(
y=level, color=TV_GREEN, linewidth=0.8,
linestyle="--", alpha=0.6,
)
if resistance_levels:
for level in resistance_levels:
ax_main.axhline(
y=level, color=TV_RED, linewidth=0.8,
linestyle="--", alpha=0.6,
)
# Pattern annotation
if show_pattern_annotation and pattern_name:
ax_main.annotate(
pattern_name,
xy=(date_nums[len(date_nums) // 2], df["high"].iloc[len(date_nums) // 2]),
fontsize=10, color=TV_YELLOW,
ha="center", va="bottom",
bbox=dict(boxstyle="round,pad=0.3", facecolor=TV_BG, edgecolor=TV_YELLOW, alpha=0.8),
)
# Title
ax_main.set_title(
f"{symbol} | {timeframe}",
color=TV_FG, fontsize=12, fontweight="bold",
)
# Volume subplot
current_ax = 1
if show_volume:
ax_vol = axes[current_ax]
vol_colors = [
TV_GREEN if df["close"].iloc[i] >= df["open"].iloc[i] else TV_RED
for i in range(len(df))
]
ax_vol.bar(date_nums, df["volume"], width, color=vol_colors, alpha=0.7)
ax_vol.set_ylabel("Volume", color=TV_FG, fontsize=8)
if "volume_sma_20" in df.columns:
ax_vol.plot(
date_nums, df["volume_sma_20"],
color=TV_ORANGE, linewidth=0.6, alpha=0.7,
)
current_ax += 1
# RSI subplot
if indicators and "rsi" in indicators and "rsi_14" in df.columns:
ax_rsi = axes[current_ax]
ax_rsi.plot(date_nums, df["rsi_14"], color=TV_PURPLE, linewidth=0.8)
ax_rsi.axhline(y=70, color=TV_RED, linewidth=0.5, linestyle="--", alpha=0.5)
ax_rsi.axhline(y=30, color=TV_GREEN, linewidth=0.5, linestyle="--", alpha=0.5)
ax_rsi.fill_between(date_nums, 70, 100, color=TV_RED, alpha=0.05)
ax_rsi.fill_between(date_nums, 0, 30, color=TV_GREEN, alpha=0.05)
ax_rsi.set_ylim(0, 100)
ax_rsi.set_ylabel("RSI", color=TV_FG, fontsize=8)
current_ax += 1
# MACD subplot
if indicators and "macd" in indicators and "macd" in df.columns:
ax_macd = axes[current_ax]
ax_macd.plot(date_nums, df["macd"], color=TV_BLUE, linewidth=0.8, label="MACD")
ax_macd.plot(date_nums, df["macd_signal"], color=TV_ORANGE, linewidth=0.8, label="Signal")
macd_hist = df["macd_histogram"]
hist_colors = [TV_GREEN if v >= 0 else TV_RED for v in macd_hist]
ax_macd.bar(date_nums, macd_hist, width, color=hist_colors, alpha=0.5)
ax_macd.axhline(y=0, color=TV_FG, linewidth=0.3)
ax_macd.set_ylabel("MACD", color=TV_FG, fontsize=8)
ax_macd.legend(
loc="upper left", fontsize=7,
facecolor=TV_BG, edgecolor=TV_GRID, labelcolor=TV_FG,
)
current_ax += 1
# X-axis formatting
ax = axes[-1]
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))
ax.xaxis.set_major_locator(mdates.AutoDateLocator())
plt.xticks(rotation=30, ha="right")
# Legend (only if there are labeled artists)
handles, labels = ax_main.get_legend_handles_labels()
if handles:
ax_main.legend(
loc="upper left", fontsize=7,
facecolor=TV_BG, edgecolor=TV_GRID, labelcolor=TV_FG,
)
plt.tight_layout()
fig.savefig(output_path, dpi=dpi, bbox_inches="tight", facecolor=TV_BG)
plt.close(fig)
return output_path
def generate_chart_grid(
df: pd.DataFrame,
symbol: str,
timeframe: str,
output_path: str,
n_candles: int = 60,
start_idx: int = 0,
) -> str:
"""Generate a clean chart with a specific window of candles."""
end_idx = min(start_idx + n_candles, len(df))
window = df.iloc[start_idx:end_idx]
if len(window) < 20:
return ""
return generate_chart_image(
window, symbol, timeframe, output_path,
show_volume=True,
show_sma=[20, 50],
show_ema=[9],
show_bollinger=False,
show_vwap=False,
)
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