Building a Complete Algorithmic Trading Dashboard with quantstats

Algorithmic trading has become an essential tool for many traders and financial analysts. With the right tools, traders can implement automated strategies that can execute orders much faster than any human. In this article, we’ll walk through how to build a complete algorithmic trading dashboard using quantstats, a Python library that provides a wealth of tools for quantitative analysis and portfolio performance.

What is QuantStats?

QuantStats is a Python library designed for financial analysis and backtesting of trading strategies. It offers a collection of metrics that provide insights into the performance of your strategies and helps in decision-making. It is built on top of other popular libraries such as NumPy, Pandas, and matplotlib, which makes it both powerful and flexible.

Setting Up Your Environment

Before we dive into coding, we need to ensure that our environment is set up with the necessary tools and libraries. You’ll need Python 3.6 or later and some essential libraries:

pip install numpy pandas matplotlib quantstats

Once you have all these dependencies installed, we are ready to start building our trading dashboard.

Getting Your Data

Your trading dashboard starts with acquiring and processing market data. This can be done using various APIs such as Alpha Vantage, IEX Cloud, or you can use historical datasets for backtesting.

import pandas as pd
import yfinance as yf

# Fetch historical data for a stock, e.g., Apple
symbol = 'AAPL'
data = yf.download(symbol, start='2020-01-01', end='2023-10-01')
print(data.head())

Analyzing Your Trading Strategy

With quantstats, once you have your data, you can start performing quantitative analysis on your trading strategy. Let’s assume you have a strategy that issues buy and sell signals stored in a Pandas DataFrame.

import quantstats as qs

# Assuming you have a strategy_returns that calculates returns
strategy_returns = ...  # Populate this with your strategy return series

# Create a quantstats report
qs.reports.html(strategy_returns, output='report.html', title='My Strategy Performance')

The code above will use QuantStats to generate an HTML report that includes performance graphs, strategic metrics, and risk information about your trading strategy.

Visualizing Portfolio Performance

Quantstats offers several visualization tools to assess portfolio performance. To plot the performance of your portfolio, utilize the plotting functions. Here’s an example of how to create a cumulative returns chart:

qs.plots.returns(strategy_returns)

You can similarly access other graphical representations such as monthly heatmaps and portfolio risk plots.

Creating a Dashboard

To bring everything together, we’ll use Dash by Plotly — a framework that allows you to build web applications with Python. First, install Dash:

pip install dash dash-core-components dash-html-components

Next, create a basic layout for our dashboard:

import dash
from dash import html, dcc
import dash_core_components as dcc
import plotly.graph_objs as go

app = dash.Dash(__name__)

app.layout = html.Div(children=[
    html.H1(children='Algorithmic Trading Dashboard'),
    dcc.Graph(
        figure=go.Figure(data=[
            go.Line(x=data.index, y=data['Close'], name='Stock Prices')
        ])
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

This code initializes a web application with a single line plot displaying stock closing prices. You can extend this by adding more graphs and components, dynamically integrating quantstats results, and providing interactive features to analyze different trading scenarios.

Conclusion

Building an algorithmic trading dashboard involves a combination of tools for retrieving data, analyzing it, and visualizing results effectively. Quantstats makes strategy evaluation simple and insightful, while Dash allows those insights to be presented interactively. Combined, these tools enable traders and analysts to build sophisticated dashboards tailored to their strategy needs.