Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Yes
API Access
Has API
Yes
Integrations
Binance
No
Gate.io
No
Google Maps
Yes
JavaScript
Yes
Kraken
No
Python
Yes
Telegram
No
Integrations
Binance
Yes
Gate.io
Yes
Google Maps
No
JavaScript
No
Kraken
Yes
Python
No
Telegram
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Bokeh
Website
bokeh.org
Vendor Details
Company Name
Freqtrade
Website
www.freqtrade.io/en/stable/