Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
FlowMap
BookMap
Atas
GoCharting
MotiveWave
Sierra Chart
FlowMap is built to be minimal, yet a powerful orderflow tool for TradingView. FlowMap can be used on all markets and timeframes with advanced orderflow features, analytics with historical likelihoods as well as automation with alerts and scans.
๐ง Liquidity Heatmap Find deep liquidity. See where traders are likely setting large buy and sell orders, ready to be swept.
๐ Internal Flow See unusual volume with an X-ray view through candles. Detect aggressive high value orders hiding in inside candles.
๐ Value Area & POC Follow the flows. See where majority of traders executed their trades and where the pinnacle of interest is located.
๐ฅ Liquidations Spot rekt traders with significant stop-loss triggered liquidations and identify when the path of least resistance is turning.
๐ Backtesting Validate trade ideas with data. See historical volume and price impact for any single orderflow event or combine multiple ones.
๐ Alerts Stop skimming through charts manually. Create a custom alert and get notified when flows are turning.
๐ Market Scans Automate finding opportunities. Scan hundreds of charts at once for volume events.
Spot trapped traders See when traders are absorbed into limit orders and get trapped. Ride the loss-cover fueled squeeze.
Catch trends early Catch smart money initiating trends with conviction using aggressive hidden flows.
Ride breakouts Join strong flows moving the markets on liquidity pool breakouts.
Find pain trades Counter-trade liquidated traders puking their positions and exhausting price, turning path of least resistance to opposite direction.
Identify key levels Predict in advance where price will find resistance using liquidity pools.
Matplotlib FlowMapFlowMap's answer:
FlowMap offers advanced orderflow for TradingView - liquidations, liquidity heatmap, value area, point of control and internal flow. FlowMap supports features such as backtesting historical price and volume impact for orderflow events, creating custom alerts and scanning hundreds of markets for flows.
FlowMap's answer:
FlowMap offers the same capabilities seen on native orderflow platforms for TradingView on a plug-n-play basis. No downloads, no setups, no volume feeds, no volume tick size adjustments. Add to chart and start using on any market and timeframe on TradingView.
FlowMap's answer:
FlowMap caters to traders seeking to gain insight into primary driving force of markets - volume. In simple terms, with FlowMap you see under the hood of charts, not just the chart.
FlowMap's answer:
FlowMap is built on 4 years of diligent study of price and volume, through developing open source volume and orderflow indicators for TradingView. Some of our free indicators have gained large popularity and been awarded with Editors Picks' features. FlowMap is our flagship product, representing state-of-the-art solution our free indicators provide.
FlowMap's answer:
FlowMap is built on top of TradingView using PineScript.
FlowMap's answer:
FlowMap caters to orderflow and volume traders seeking to gain more detail and information on financial market flows that drive prices.
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
BookMap - Drastically decrease the time taken to organise bookmarks ๐
NumPy - NumPy is the fundamental package for scientific computing with Python
Atas - ATAS professional trading and analytic platform. ะTAS analysis program: time&sales (time and sales), smart tape, atas allows to analyze point volumes
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
GoCharting - GoCharting is a modern financial analytics platform offering world-class trading and charting experience.
