TradingView Lightweight Historical Charts using OpenAlgo – Python Tutorial

If you’re a trader or developer working with Indian stock market data, you’ve likely faced one of two problems:

Struggling to visualize stock data effectively
Wrestling with APIs or charting libraries that are overly complex

In this guide, we combine the OpenAlgo API with TradingView-style Lightweight Charts in Python to solve both.

We’ll walk through seven different examples, each focusing on a use case—from simple candlesticks to dynamic symbols, drawing tools, and multi-symbol comparison.

This article is code-heavy and practical. By the end, you’ll be able to build high-performance, interactive financial charts using Python with Indian market data.

What Is OpenAlgo?

OpenAlgo is a powerful platform that provides APIs for 22+ Indian brokers like Angel One, Zerodha, Fyers, Upstox, Dhan, Kotak, and more. You can fetch historical and live market data, place orders, access Realtime streaming quotes, and much more.

For these examples, we use client.history() to fetch historical bar data for NSE stocks.

What Are Lightweight Charts?

Lightweight Charts by TradingView is a modern charting library optimized for performance and clarity. The Python wrapper we use enables native integration with Pandas data and UI interactivity in one line.

Prerequisites

Install the following packages:

pip install openalgo lightweight-charts pandas

Make sure your OpenAlgo server is running locally (or remotely), and you have a valid api_key.

Example 1: Basic Candlestick Chart

This is the simplest version. It downloads data for RELIANCE using OpenAlgo and renders it in a clean chart.

What you get:

Full OHLCV data
No overlays or indicators
JSON-serializable time formatting

This is perfect as a starting template or sanity check for your data.

Python Code

import pandas as pd
from datetime import datetime, timedelta
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(api_key='your-openalgo-apikey', host='http://127.0.0.1:5000')

def prepare_openalgo_chart_data(symbol="RELIANCE", exchange="NSE", interval="5m", days=5):
    from datetime import datetime, timedelta

    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    df = client.history(
        symbol=symbol,
        exchange=exchange,
        interval=interval,
        start_date=start_date.strftime("%Y-%m-%d"),
        end_date=end_date.strftime("%Y-%m-%d")
    )

    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    df_reset = df.reset_index()

    # Detect the datetime column
    datetime_col = next(
        (col for col in df_reset.columns if pd.api.types.is_datetime64_any_dtype(df_reset[col])),
        None
    )

    if datetime_col is None:
        raise ValueError("No datetime column found")

    has_time = any(df_reset[datetime_col].dt.hour != 0)

    df_reset['time'] = df_reset[datetime_col].dt.strftime(
        '%Y-%m-%d %H:%M:%S' if has_time else '%Y-%m-%d'
    )

    result_df = pd.DataFrame({
        'time': df_reset['time'],
        'open': df_reset['open'],
        'high': df_reset['high'],
        'low': df_reset['low'],
        'close': df_reset['close'],
        'volume': df_reset['volume']
    })

    return result_df

if __name__ == "__main__":
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    # Prepare data for the chart
    chart_data = prepare_openalgo_chart_data(symbol="RELIANCE", exchange="NSE", interval="5m", days=30)

    # Plot using lightweight-charts
    chart = Chart()
    chart.set(chart_data)
    chart.show(block=True)

Example 2: Add Simple Moving Average (SMA)

Here we calculate a 50-period SMA and overlay it on the chart as a colored line.

This is useful for:

Trend-following strategies
Technical analysis
Visual support/resistance detection

The overlay is clean, optimized for performance, and uses Pandas’ rolling().mean() under the hood.

Python Code

import pandas as pd
from datetime import datetime, timedelta
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(api_key='your-openalgo-apikey', host='http://127.0.0.1:5000')

def fetch_openalgo_data(symbol="RELIANCE", exchange="NSE", interval="1d", days=365):
    """
    Fetch historical data from OpenAlgo
    """
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    response = client.history(
        symbol=symbol,
        exchange=exchange,
        interval=interval,
        start_date=start_date.strftime("%Y-%m-%d"),
        end_date=end_date.strftime("%Y-%m-%d")
    )

    # Check if response is valid DataFrame
    if not isinstance(response, pd.DataFrame):
        print("❌ Failed to fetch data:", response)
        raise ValueError("OpenAlgo did not return a DataFrame")

    if response.empty:
        raise ValueError("Received empty data from OpenAlgo.")

    # Ensure datetime index
    if not isinstance(response.index, pd.DatetimeIndex):
        response.index = pd.to_datetime(response.index)

    return response


def prepare_data_for_chart(df):
    """
    Format OpenAlgo DataFrame for lightweight-charts
    """
    df_reset = df.reset_index()

    # Find datetime column
    datetime_col = next(
        (col for col in df_reset.columns if pd.api.types.is_datetime64_any_dtype(df_reset[col])),
        None
    )
    if datetime_col is None:
        raise ValueError("No datetime column found.")

    df_reset['time'] = df_reset[datetime_col].dt.strftime('%Y-%m-%d')

    return pd.DataFrame({
        'time': df_reset['time'],
        'open': df_reset['open'],
        'high': df_reset['high'],
        'low': df_reset['low'],
        'close': df_reset['close'],
        'volume': df_reset['volume']
    })

def calculate_sma(df, period=50):
    """
    Calculate Simple Moving Average for lightweight-charts
    """
    sma_values = df['close'].rolling(window=period).mean()
    sma_df = pd.DataFrame({
        'time': df.index.strftime('%Y-%m-%d'),
        f'SMA {period}': sma_values
    })
    return sma_df.dropna()

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    # Fetch data
    df = fetch_openalgo_data(symbol="RELIANCE", exchange="NSE", interval="D", days=365)

    # Prepare chart data
    chart_data = prepare_data_for_chart(df)

    # Calculate SMA
    sma50_data = calculate_sma(df, 50)

    # Create chart
    chart = Chart()
    chart.legend(visible=True)
    chart.set(chart_data)

    # Create SMA line
    sma_line = chart.create_line('SMA 50', '#FF5733')
    sma_line.set(sma50_data)

    chart.show(block=True)

Example 3: RSI with Subchart

This example adds a Relative Strength Index (RSI) indicator in a separate subpanel below the main chart.

Highlights:

Uses subcharts for better separation of logic
Shows RSI(14) values with horizontal markers at 30 and 70
Watermarked with symbol and indicator name

Ideal for building momentum-based strategies or dashboards.

Python Code

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(
    api_key='your-openalgo-apikey',
    host='http://127.0.0.1:5000'
)

def fetch_openalgo_data(symbol="RELIANCE", exchange="NSE", interval="D", days=365):
    """
    Fetch historical data from OpenAlgo
    """
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    response = client.history(
        symbol=symbol,
        exchange=exchange,
        interval=interval,
        start_date=start_date.strftime("%Y-%m-%d"),
        end_date=end_date.strftime("%Y-%m-%d")
    )

    if not isinstance(response, pd.DataFrame):
        print("❌ Failed to fetch data:", response)
        raise ValueError("OpenAlgo did not return a DataFrame")

    if not isinstance(response.index, pd.DatetimeIndex):
        response.index = pd.to_datetime(response.index)

    return response

def prepare_data_for_chart(df):
    """
    Format OpenAlgo DataFrame for lightweight-charts
    """
    df_reset = df.reset_index()
    datetime_col = next(
        (col for col in df_reset.columns if pd.api.types.is_datetime64_any_dtype(df_reset[col])),
        None
    )
    df_reset['time'] = df_reset[datetime_col].dt.strftime('%Y-%m-%d')

    return pd.DataFrame({
        'time': df_reset['time'],
        'open': df_reset['open'],
        'high': df_reset['high'],
        'low': df_reset['low'],
        'close': df_reset['close'],
        'volume': df_reset['volume']
    })

def calculate_rsi(df, period=14):
    """
    Calculate RSI from OpenAlgo close prices
    """
    close = df['close']
    delta = close.diff()

    gain = delta.where(delta > 0, 0)
    loss = -delta.where(delta < 0, 0)

    avg_gain = gain.rolling(window=period).mean()
    avg_loss = loss.rolling(window=period).mean()

    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))

    rsi_df = pd.DataFrame({
        'time': df.index.strftime('%Y-%m-%d'),
        'RSI (14)': rsi
    })

    return rsi_df

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    symbol = "RELIANCE"
    df = fetch_openalgo_data(symbol=symbol, exchange="NSE", interval="D", days=365)

    chart_data = prepare_data_for_chart(df)
    rsi_data = calculate_rsi(df, 14)

    # Create main chart (candlestick + volume)
    chart = Chart(inner_width=1, inner_height=0.7)
    chart.legend(visible=True)

    chart.layout(
        background_color='#151924',
        text_color='#FFFFFF',
        font_size=14,
        font_family='Arial'
    )

    chart.candle_style(
        up_color='#26a69a', 
        down_color='#ef5350',
        border_up_color='#26a69a', 
        border_down_color='#ef5350',
        wick_up_color='#26a69a', 
        wick_down_color='#ef5350'
    )

    chart.volume_config(
        up_color='rgba(38, 166, 154, 0.5)', 
        down_color='rgba(239, 83, 80, 0.5)'
    )

    # RSI Subchart (bottom panel)
    rsi_subchart = chart.create_subchart(width=1, height=0.3, sync=True)

    chart.set(chart_data)
    rsi_line = rsi_subchart.create_line('RSI (14)', '#8A2BE2')
    rsi_line.set(rsi_data)

    rsi_subchart.horizontal_line(70, color='#FF6B6B')
    rsi_subchart.horizontal_line(30, color='#4CAF50')

    chart.watermark(symbol, color='rgba(255, 255, 255, 0.1)')
    rsi_subchart.watermark('RSI (14)', color='rgba(255, 255, 255, 0.1)')

    chart.show(block=True)

Example 4: Dual Chart Comparison (INFY vs TCS)

Want to compare two stocks side by side? This chart renders two candlestick charts—INFY and TCS—horizontally split.

Why it’s useful:

Compare sector stocks (e.g., Infosys vs TCS)
Visual divergence detection
Independent navigation per chart

The layout remains responsive, and each chart maintains its styling and watermark.

Python Code

import pandas as pd
from datetime import datetime, timedelta
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(
    api_key='your-openalgo-apikey',
    host='http://127.0.0.1:5000'
)

def fetch_openalgo_data(symbol, exchange="NSE", interval="D", days=365):
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    df = client.history(
        symbol=symbol,
        exchange=exchange,
        interval=interval,
        start_date=start_date.strftime("%Y-%m-%d"),
        end_date=end_date.strftime("%Y-%m-%d")
    )

    if not isinstance(df, pd.DataFrame):
        raise ValueError(f"Error fetching data for {symbol}: {df}")

    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    return df

def prepare_data_for_chart(df):
    df_reset = df.reset_index()

    datetime_col = next(
        (col for col in df_reset.columns if pd.api.types.is_datetime64_any_dtype(df_reset[col])),
        None
    )

    df_reset['time'] = df_reset[datetime_col].dt.strftime('%Y-%m-%d')

    return pd.DataFrame({
        'time': df_reset['time'],
        'open': df_reset['open'],
        'high': df_reset['high'],
        'low': df_reset['low'],
        'close': df_reset['close'],
        'volume': df_reset['volume']
    })

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    # Fetch and prepare data for INFY and TCS
    infy_df = fetch_openalgo_data("INFY", "NSE", "D", 365)
    tcs_df = fetch_openalgo_data("TCS", "NSE", "D", 365)

    infy_data = prepare_data_for_chart(infy_df)
    tcs_data = prepare_data_for_chart(tcs_df)

    # Create side-by-side charts
    chart = Chart(inner_width=0.5, inner_height=1)
    chart2 = chart.create_subchart(position='right', width=0.5, height=1)

    # Styling
    for c in [chart, chart2]:
        c.layout(
            background_color='#151924',
            text_color='#FFFFFF',
            font_size=14,
            font_family='Arial'
        )
        c.candle_style(
            up_color='#26a69a', 
            down_color='#ef5350',
            border_up_color='#26a69a', 
            border_down_color='#ef5350',
            wick_up_color='#26a69a', 
            wick_down_color='#ef5350'
        )
        c.volume_config(
            up_color='rgba(38, 166, 154, 0.5)', 
            down_color='rgba(239, 83, 80, 0.5)'
        )
        c.legend(visible=True)

    # Set data and labels
    chart.set(infy_data)
    chart2.set(tcs_data)
    chart.watermark('INFY', color='rgba(255, 255, 255, 0.1)')
    chart2.watermark('TCS', color='rgba(255, 255, 255, 0.1)')

    chart.show(block=True)

Example 5: Toolbox with Drawing Tools

This version enables drawing tools for manual analysis:

Trendlines
Rays
Horizontal lines
Rectangles

And it can export drawings to JSON for later use.

This is ideal for traders who rely on manual annotations or want to simulate a TradingView-like UI.

Python Code

import pandas as pd
from datetime import datetime, timedelta
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(
    api_key='your-openalgo-apikey',
    host='http://127.0.0.1:5000'
)

def fetch_openalgo_data(symbol, exchange="NSE", interval="D", days=365):
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    df = client.history(
        symbol=symbol,
        exchange=exchange,
        interval=interval,
        start_date=start_date.strftime("%Y-%m-%d"),
        end_date=end_date.strftime("%Y-%m-%d")
    )

    if not isinstance(df, pd.DataFrame):
        raise ValueError(f"Error fetching data for {symbol}: {df}")

    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    return df

def prepare_data_for_chart(df):
    df_reset = df.reset_index()

    datetime_col = next(
        (col for col in df_reset.columns if pd.api.types.is_datetime64_any_dtype(df_reset[col])),
        None
    )

    df_reset['time'] = df_reset[datetime_col].dt.strftime('%Y-%m-%d')

    return pd.DataFrame({
        'time': df_reset['time'],
        'open': df_reset['open'],
        'high': df_reset['high'],
        'low': df_reset['low'],
        'close': df_reset['close'],
        'volume': df_reset['volume']
    })

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    # Select symbol
    symbol = "INFY"  # Replace with "TCS", "SBIN", etc. if needed

    # Fetch data
    df = fetch_openalgo_data(symbol=symbol, exchange="NSE", interval="D", days=365)
    chart_data = prepare_data_for_chart(df)

    # Create chart with toolbox enabled
    chart = Chart(toolbox=True)
    chart.legend(visible=True)

    chart.layout(
        background_color='#151924',
        text_color='#FFFFFF',
        font_size=14,
        font_family='Arial'
    )

    chart.candle_style(
        up_color='#26a69a', 
        down_color='#ef5350',
        border_up_color='#26a69a', 
        border_down_color='#ef5350',
        wick_up_color='#26a69a', 
        wick_down_color='#ef5350'
    )

    chart.set(chart_data)
    chart.watermark(symbol, color='rgba(255, 255, 255, 0.1)')

    print("\n📐 Drawing Tools Available:")
    print("1. Click the toolbox button in the top-right corner")
    print("2. Select: Trendline, Horizontal Line, Ray, Rectangle")
    print("3. Draw directly on the chart")

    chart.show(block=True)

    # Export drawings on close
    chart.toolbox.export_drawings('drawings.json')
    print("✅ Drawings saved to drawings.json")

Example 6: Dynamic Symbol + Timeframe Switcher

This is a fully dynamic, production-grade chart:

Search box for changing symbols (e.g., INFY, SBIN)
Switcher to toggle between 1m, 5m, 15m, 1h, D
Uses only OpenAlgo-supported intervals
Chart is auto-updated when symbol or timeframe changes

It also includes dynamic watermarking, JSON-safe time formatting, and drawing preservation.

Python Code

import pandas as pd
import datetime as dt
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo client
client = api(
    api_key='your-openalgo-apikey',
    host='http://127.0.0.1:5000'
)

def get_data_for_timeframe(symbol, timeframe):
    """Fetch OpenAlgo historical data for given symbol and timeframe (fixed NSE exchange)."""
    end_date = dt.datetime.now()
    days_map = {
        '1m': 2,
        '5m': 15,
        '15m': 30,
        '1h': 90,
        'D': 730
    }
    start_date = end_date - dt.timedelta(days=days_map.get(timeframe, 30))

    df = client.history(
        symbol=symbol,
        exchange="NSE",
        interval=timeframe,
        start_date=start_date.strftime('%Y-%m-%d'),
        end_date=end_date.strftime('%Y-%m-%d')
    )

    if not isinstance(df, pd.DataFrame) or df.empty:
        print(f"⚠️ No data for {symbol} [{timeframe}]")
        return pd.DataFrame()

    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    return df

def prepare_data_for_chart(df):
    df = df.reset_index()
    date_col = next((col for col in df.columns if pd.api.types.is_datetime64_any_dtype(df[col])), None)
    if date_col is None:
        return pd.DataFrame()

    has_time = df[date_col].dt.hour.ne(0).any()
    time_format = '%Y-%m-%d %H:%M:%S' if has_time else '%Y-%m-%d'
    df['time'] = df[date_col].dt.strftime(time_format)

    return df[['time', 'open', 'high', 'low', 'close', 'volume']]

def on_timeframe_selection(chart):
    symbol = chart.topbar['symbol'].value
    timeframe = chart.topbar['timeframe'].value

    df = get_data_for_timeframe(symbol, timeframe)
    if df.empty:
        return

    chart_data = prepare_data_for_chart(df)
    chart.set(chart_data, keep_drawings=True)
    chart.watermark(f"{symbol} - NSE - {timeframe}", color='rgba(255, 255, 255, 0.1)')

def on_symbol_search(chart, searched_string):
    chart.topbar['symbol'].set(searched_string)
    on_timeframe_selection(chart)

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    default_symbol = "RELIANCE"
    default_timeframe = "D"

    chart = Chart(inner_width=1, inner_height=1)
    chart.topbar.textbox('symbol', default_symbol)
    chart.topbar.switcher(
        'timeframe', ('1m', '5m', '15m', '1h', 'D'),
        default=default_timeframe,
        func=on_timeframe_selection
    )
    chart.events.search += on_symbol_search

    chart.layout(
        background_color='#151924',
        text_color='#FFFFFF',
        font_size=12,
        font_family='Arial'
    )
    chart.candle_style(
        up_color='#26a69a',
        down_color='#ef5350',
        border_up_color='#26a69a',
        border_down_color='#ef5350',
        wick_up_color='#26a69a',
        wick_down_color='#ef5350'
    )
    chart.volume_config(
        up_color='rgba(38, 166, 154, 0.5)',
        down_color='rgba(239, 83, 80, 0.5)'
    )

    # Load default data
    df = get_data_for_timeframe(default_symbol, default_timeframe)
    chart_data = prepare_data_for_chart(df)
    chart.set(chart_data)

    chart.watermark(f"{default_symbol} - NSE - {default_timeframe}", color='rgba(255, 255, 255, 0.1)')
    chart.show(block=True)

Example 7: OpenAlgo Dynamic Symbols + Timeswitcher and Drawing Tool

The final example rewrites a Dynamic Symbols + Timeswitcher and Drawing Tool based TradingView chart to use OpenAlgo symbols and exchanges directly.

Benefits:

Works for all NSE symbols like TCS, INFY, BANKNIFTY, etc.
Fully mapped to OpenAlgo-compatible formats

This is the best way to future-proof your charting tool for real market data with OpenAlgo infrastructure.

Python Code

import datetime as dt
import pandas as pd
from lightweight_charts import Chart
from openalgo import api

# Initialize OpenAlgo API
client = api(
    api_key='your-openalgo-apikey',
    host='http://127.0.0.1:5000'
)

def prepare_data_for_chart(df, verbose=False):
    df = df.copy()

    # Ensure datetime index
    if not isinstance(df.index, pd.DatetimeIndex):
        df.index = pd.to_datetime(df.index)

    # Reset index to get datetime column
    df = df.reset_index()
    df = df.rename(columns={df.columns[0]: 'time'})

    # Convert to string for lightweight_charts
    df['time'] = df['time'].dt.strftime('%Y-%m-%d %H:%M:%S' if df['time'].dt.hour.ne(0).any() else '%Y-%m-%d')

    return df[['time', 'open', 'high', 'low', 'close', 'volume']]

def get_bar_data(symbol, timeframe):
    """Fetch bar data from OpenAlgo"""
    now = dt.datetime.now()
    days_map = {
        '1m': 2,
        '5m': 15,
        '30m': 30,
        'D': 365,
        'W': 730
    }
    interval_map = {
        '1m': '1m',
        '5m': '5m',
        '30m': '30m',
        '1d': 'D',
        '1wk': 'W'
    }

    interval = interval_map.get(timeframe)
    if not interval:
        print(f"⚠ Unsupported timeframe: {timeframe}")
        return False

    start_date = now - dt.timedelta(days=days_map[interval])
    chart.spinner(True)
    try:
        df = client.history(
            symbol=symbol,
            exchange='NSE',
            interval=interval,
            start_date=start_date.strftime('%Y-%m-%d'),
            end_date=now.strftime('%Y-%m-%d')
        )
    except Exception as e:
        print(f"❌ Error fetching data: {e}")
        chart.spinner(False)
        return False

    chart.spinner(False)

    if not isinstance(df, pd.DataFrame) or df.empty:
        print(f"⚠ No data received for {symbol}")
        return False

    chart_data = prepare_data_for_chart(df)
    chart.set(chart_data)
    return True

def on_search(chart, searched_string):
    if get_bar_data(searched_string, chart.topbar['timeframe'].value):
        chart.topbar['symbol'].set(searched_string)

def on_timeframe_selection(chart):
    get_bar_data(chart.topbar['symbol'].value, chart.topbar['timeframe'].value)

if __name__ == '__main__':
    print("🔁 OpenAlgo Python Bot is Downloading Data.")

    chart = Chart(toolbox=True)
    chart.legend(True)
    chart.events.search += on_search
    chart.topbar.textbox('symbol', 'TCS')
    chart.topbar.switcher(
        'timeframe',
        ('1m', '5m', '30m', '1d', '1wk'),
        default='5m',
        func=on_timeframe_selection
    )

    # Load default data at startup
    get_bar_data('TCS', '5m')

    chart.show(block=True)

Supported Timeframes in OpenAlgo

Only these intervals are officially supported:

Seconds: 5s, 10s, 15s, 30s, 45s
Minutes: 1m, 2m, 3m, 5m, 10m, 15m, 20m, 30m
Hours: 1h, 2h, 4h
Daily: D
Weekly: W
Monthly: M

All charts shown in this guide use 1m, 5m, 15m, 1h, and D to avoid API compatibility issues. Different brokers support different timeframes. Check your broker supported interval and enter the timeframe value accordingly.

With OpenAlgo and lightweight-charts, you can now build:

High-speed chart dashboards
Strategy testers with overlays
Real-time market visualizers
Personal trading terminals

For production apps, you can integrate these with Flask, FastAPI, or any Electron frontend.

Where to Go Next?

📘 Official Docs: https://docs.openalgo.in
💬 Developer Community: https://openalgo.in/discord

TradingView Lightweight Historical Charts using OpenAlgo – Python Tutorial

What Is OpenAlgo?

What Are Lightweight Charts?

Prerequisites

Example 1: Basic Candlestick Chart

Example 2: Add Simple Moving Average (SMA)

Example 3: RSI with Subchart

Example 4: Dual Chart Comparison (INFY vs TCS)

Example 5: Toolbox with Drawing Tools

Example 6: Dynamic Symbol + Timeframe Switcher

Example 7: OpenAlgo Dynamic Symbols + Timeswitcher and Drawing Tool

Supported Timeframes in OpenAlgo

Where to Go Next?

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