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Description

Bokeh simplifies the creation of standard visualizations while also accommodating unique or specialized scenarios. It allows users to publish plots, dashboards, and applications seamlessly on web pages or within Jupyter notebooks. The Python ecosystem boasts a remarkable collection of robust analytical libraries such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV. With its extensive selection of widgets, plotting tools, and user interface events that can initiate genuine Python callbacks, the Bokeh server serves as a vital link, enabling the integration of these libraries into dynamic, interactive visualizations accessible via the browser. Additionally, Microscopium, a project supported by researchers at Monash University, empowers scientists to uncover new functions of genes or drugs through the exploration of extensive image datasets facilitated by Bokeh’s interactive capabilities. Another useful tool, Panel, which is developed by Anaconda, enhances data presentation by leveraging the Bokeh server. It streamlines the creation of custom interactive web applications and dashboards by linking user-defined widgets to a variety of elements, including plots, images, tables, and textual information, thus broadening the scope of data interaction possibilities. This combination of tools fosters a rich environment for data analysis and visualization, making it easier for researchers and developers to share their insights.

Description

Craft your trading strategy in Python, utilizing the pandas library for data manipulation. For inspiration, explore example strategies that are available in the strategy repository. Begin by downloading the historical data for the exchange along with the specific markets you're interested in trading. Once you have the data, rigorously test your strategy against it. Employ hyperoptimization techniques, leveraging machine learning approaches, to identify the optimal parameters for your strategy, focusing on aspects such as entry points, exit strategies, ROI targets, stop-loss limits, and trailing stop-loss configurations. The objective is to maximize the historical trade expectancy across different markets by adjusting stop-loss parameters, subsequently determining which markets to trade in. The trade size should reflect a calculated percentage of your overall capital at risk. To gain further insights, conduct additional analyses using either the backtesting results or the trading history stored in a SQL database from Freqtrade, which can include automated plotting functions and ways to visualize the data within interactive environments. Ultimately, a comprehensive understanding of your strategy's performance is essential for informed decision-making in trading.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Binance
Gate.io
Google Maps
JavaScript
Kraken
Python
Telegram

Integrations

Binance
Gate.io
Google Maps
JavaScript
Kraken
Python
Telegram

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Bokeh

Website

bokeh.org

Vendor Details

Company Name

Freqtrade

Website

www.freqtrade.io/en/stable/

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Product Features

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