In today's information-driven world, access to reliable and comprehensive news data is crucial for informed decision-making across various domains. Introducing the Fetch NewsAPI Connector - a powerful worker that provides seamless access to one of the most comprehensive news databases available through EventRegistry's NewsAPI.
The Fetch NewsAPI Connector integrates with EventRegistry's NewsAPI to provide access to millions of news articles from thousands of trusted sources worldwide. Whether you're building news aggregators, conducting sentiment analysis, performing market research, or developing AI-powered content systems, this connector offers everything you need in one unified interface.
With advanced filtering capabilities by categories, sources, languages, and date ranges, the Fetch NewsAPI connector eliminates the need for multiple news APIs and complex data aggregation pipelines.
Article Retrieval: Fetches news articles using complex queries with category and source filtering
Temporal Filtering: Supports date range queries for historical and current news analysis
Source Validation: Ensures articles come from reputable news sources
Language Filtering: Retrieves articles in specified languages
Result Limiting: Configurable result limits with intelligent ranking
The Fetch NewsAPI Connector represents a comprehensive solution for news data access, providing everything from breaking financial news to historical archives in a single, easy-to-use interface. Whether you're a financial analyst, data scientist, content creator, or developer building news applications, this connector offers the flexibility and reliability you need to build sophisticated news-driven workflows.
With support for global news sources, advanced filtering capabilities, and seamless integration with AI analysis tools like VectorAnalyzer, the Fetch NewsAPI connector eliminates news data silos and simplifies the development of intelligent news processing systems. Start exploring the power of comprehensive news data today and unlock new possibilities for your news analysis and content processing applications.
For detailed guides on specific use cases, check out our dedicated articles covering advanced filtering techniques, AI-powered news analysis workflows, and real-time news monitoring strategies with step-by-step interface instructions and practical examples.
In today's data-rich environment, finding relevant information among vast collections of text requires more than simple keyword matching. Introducing the VectorAnalyzer Connector - an advanced AI-powered semantic search engine that understands meaning, context, and sentiment to deliver intelligent content analysis and ranking.
The VectorAnalyzer Connector transforms traditional text search by using cutting-edge AI technology to understand semantic meaning rather than just matching keywords. Whether you're analyzing news articles, research documents, customer feedback, or any text collection, this connector provides intelligent similarity scoring, sentiment analysis, and dynamic filtering to surface the most relevant content.
Built on state-of-the-art language models and vector databases, the VectorAnalyzer delivers enterprise-grade semantic search capabilities with real-time processing and flexible ranking options.
Vector Embedding Generation: Converts text into mathematical vectors using advanced transformer models
Similarity Calculation: Uses cosine similarity to measure semantic relatedness (0.0-1.0 scale)
Dynamic Filtering: Automatically determines quality thresholds based on result distribution
Sentiment Classification: Analyzes emotional tone using fine-tuned language models
Intelligent Ranking: Combines similarity scores with optional date-based sorting
The VectorAnalyzer Connector represents a quantum leap in text search and analysis capabilities, moving beyond traditional keyword matching to true semantic understanding. Whether you're building news monitoring systems, content recommendation engines, research platforms, or intelligence analysis tools, this connector provides the AI-powered foundation you need for next-generation text analysis.
With support for advanced vector embeddings, intelligent similarity scoring, sentiment analysis, and flexible ranking options, the VectorAnalyzer eliminates the limitations of traditional search while delivering enterprise-grade performance and accuracy. Start exploring the power of semantic search today and unlock new possibilities for understanding and analyzing text data at scale.
For detailed guides on specific use cases, check out our dedicated articles covering advanced semantic search techniques, sentiment analysis workflows, and AI-powered content processing pipelines with step-by-step interface instructions and practical examples.
In today's fast-paced financial markets, access to reliable and comprehensive market data is essential for informed trading and investment decisions. Introducing the Twelve Data Market Data Connector - a powerful worker that provides seamless access to one of the most comprehensive financial data APIs available.
The Twelve Data Market Data Connector integrates with the Twelve Data API to provide access to real-time and historical financial data across multiple asset classes including stocks, forex, cryptocurrencies, ETFs, and mutual funds. Whether you're building trading strategies, conducting fundamental analysis, or developing financial applications, this connector offers everything you need in one unified interface.
With support for over 40 different operations ranging from historical price data to technical indicators and fundamental company information, the Twelve Data connector eliminates the need for multiple data sources and complex API integrations.
time_series: Historical and intraday OHLCV data for any symbol
quote: Real-time quote snapshots with full market information
price: Simple real-time price data for quick checks
profile: Company overview and basic information
fundamentals: Detailed financial statements and metrics
dividends, splits, earnings: Historical corporate actions
logo: Company logo URLs for branding
income_statement: Revenue, expenses, and profitability data
balance_sheet: Assets, liabilities, and equity information
cash_flow: Operating, investing, and financing cash flows
When using the shared API key, the system enforces per-user rate limits (5 calls per minute). Make sure your userId is properly configured in the context for accurate limiting.
The Twelve Data Market Data Connector represents a comprehensive solution for financial data access, providing everything from basic price information to advanced fundamental analysis in a single, easy-to-use interface. Whether you're a retail trader, institutional investor, or financial application developer, this connector offers the flexibility and reliability you need to build sophisticated financial workflows.
With support for global markets, multiple asset classes, and extensive historical data, the Twelve Data connector eliminates data silos and simplifies the development of financial applications. Start exploring the power of comprehensive market data today and unlock new possibilities for your trading and investment strategies.
For detailed guides on specific operations, check out our dedicated articles covering each major operation category with step-by-step interface instructions and practical examples.
In the dynamic world of financial markets, identifying key support and resistance levels is crucial for successful trading strategies. Introducing the Support & Resistance Calculator - a comprehensive technical analysis worker that provides multiple methods for calculating critical price levels that influence market behavior.
The Support & Resistance Calculator leverages advanced technical analysis algorithms to identify key price levels where buying and selling pressure typically converge. Unlike simple moving averages or basic indicators, this worker combines multiple proven methodologies including pivot points, Fibonacci analysis, swing detection, and psychological price levels.
Whether you're a day trader looking for intraday levels, a swing trader identifying trend continuation points, or a position trader seeking major reversal zones, this calculator provides the analytical depth you need to make informed trading decisions.
1. Classic Pivot Points - The Foundation of Technical Analysis
Method:pivot_pointsPurpose: Calculate traditional pivot points based on previous period's high, low, and close prices. Pivot points are widely used by traders to identify key support and resistance levels for the upcoming trading session. These levels act as psychological barriers where buying and selling pressure tends to converge, making them excellent reference points for entry, exit, and stop-loss placement strategies.
Method:pivot_woodiePurpose: Calculate Woodie pivot points, a modified version of traditional pivot points that gives more weight to the closing price. This method is particularly effective in trending markets where the closing price carries significant information about market sentiment and momentum. Woodie pivots provide more responsive levels that better reflect current market conditions compared to classic pivots.
Method:pivot_camarillaPurpose: Calculate Camarilla pivot points, an advanced pivot system specifically designed for intraday trading and scalping strategies. Unlike traditional pivots, Camarilla levels are calculated using a unique formula that creates tighter ranges around the current price, making them ideal for short-term traders who need precise entry and exit points within a single trading session.
Unique Features:
8 Levels: S1-S4 support and R1-R4 resistance levels
Tighter Ranges: More precise levels for scalping and day trading
L3/L4, H3/H4: Extreme levels often act as major reversal points
Method:fibonacci_retracementPurpose: Calculate Fibonacci retracement levels based on recent price swings using the mathematical golden ratio sequence. These levels help identify potential reversal points during price corrections within a larger trend. Fibonacci retracements are particularly powerful because they combine mathematical precision with market psychology, creating levels where traders naturally place orders.
Fibonacci Ratios Used:
0.236 (23.6%): Shallow retracement, often weak support/resistance
0.382 (38.2%): Common retracement level, moderate strength
0.5 (50.0%): Psychological midpoint, strong level
0.618 (61.8%): Golden ratio, very strong level
0.786 (78.6%): Deep retracement, potential reversal zone
Method:fibonacci_extensionsPurpose: Calculate Fibonacci extension levels that project potential price targets beyond the current swing range. These levels help traders identify where a trend might continue after breaking through previous highs or lows, providing objective profit targets and continuation pattern recognition. Extensions are essential for position traders who need to set realistic price objectives.
Method:ta_extremaPurpose: Identify local maxima and minima (swing highs and lows) using advanced signal processing algorithms. This method automatically detects significant turning points in price action, creating support and resistance levels based on actual market behavior rather than mathematical formulas. It's particularly valuable for swing traders who want to focus on levels that have proven their significance through price action.
Algorithm Features:
Scipy Signal Processing: Uses argrelextrema for precise swing detection
Order Parameter: Controls sensitivity (higher = fewer, stronger levels)
ATR Clustering: Groups nearby levels into consolidated zones
Method:price_channelsPurpose: Calculate Donchian price channels (also known as trading ranges) that show the highest high and lowest low over a specified period. These channels help identify trending markets and potential breakout opportunities. When price consistently hugs one side of the channel, it indicates a strong trend, while breakouts from the channel can signal major trend changes or continuation moves.
channel_length: 20 (lookback period for channel calculation)
rowsExpr: data.price_series (expression with price data)
8. Psychological Price Levels - Round Number Analysis
Method:psychological_levelsPurpose: Identify psychological price levels based on round numbers that act as significant psychological barriers in traders' minds. These levels (like 100, 1000, 5000, etc.) often cause hesitation or increased activity because they represent clean, easy-to-remember price points. Psychological levels can be more significant than technical levels because they influence the collective behavior of market participants.
Features:
Auto Step Detection: Automatically determines appropriate step size
Custom Steps: Manual step configuration for specific assets
Multi-Level: Generates multiple levels around current price
We're continuously expanding the Support & Resistance Calculator with:
Machine Learning Integration: AI-powered level validation and prediction
Intermarket Analysis: Correlation-based level confirmation across assets
Volume Profile Integration: Volume-weighted support/resistance zones
Order Flow Analysis: Real-time order book level detection
Multi-Timeframe Synthesis: Automated level alignment across timeframes
Pattern Recognition: Automatic chart pattern detection using S/R levels
Sentiment Analysis: News and social media impact on key levels
Important Disclaimer: The Support & Resistance Calculator provides technical analysis tools for informational purposes. The calculated levels and analysis generated by this tool should not be considered as professional financial, investment, or trading advice. All trading decisions should be made based on your own research, risk tolerance, and consultation with qualified financial professionals. Technical analysis is not a guarantee of future performance. Past performance does not guarantee future results. Use this tool at your own risk and responsibility.
Support and resistance levels are fundamental concepts in technical analysis that help traders identify key price levels where buying and selling pressure converge. Whether you're a beginner learning technical analysis or an experienced trader building automated strategies, the Support & Resistance Calculator provides the analytical depth you need to enhance your trading edge.
Questions about implementing support and resistance analysis? Our support team is here to help you integrate these powerful technical tools into your trading workflows! 📈📉💹
In the world of financial analysis and economic research, access to reliable economic data is crucial. Introducing the FRED Economic Data Connector - a powerful new addition to the ApudFlow platform that provides seamless access to the Federal Reserve Economic Data (FRED) database, the premier source for US economic time series data.
The FRED Economic Data Connector leverages the comprehensive FRED API maintained by the Federal Reserve Bank of St. Louis. FRED contains over 816,000 economic time series from 108 sources, making it the most comprehensive freely available database of US economic data.
Unlike traditional data providers that require expensive subscriptions, FRED provides free access to critical economic indicators, making sophisticated economic analysis accessible to everyone.
Operation:releasesPurpose: Get information about recent economic data releases
Key Parameters:
limit: Number of releases to return (default: 25, max: 1000)
Example - Recent Releases:
{ "limit":10 }
Output:
{ "count":10, "releases":[ { "id":"53", "name":"Gross Domestic Product", "press_release":true, "link":"https://www.federalreserve.gov/releases/gdp/", "notes":"The Gross Domestic Product (GDP) is the market value of goods and services produced by labor and property in the United States." } ] }
The FRED connector features intelligent autocomplete that helps you discover series IDs as you type. Simply start typing keywords like "GDP", "unemployment", "inflation", or "treasury" and get instant suggestions.
We're continuously expanding FRED connector capabilities with:
Real-time data streaming for live economic indicators
Bulk data operations for large historical datasets
Advanced analytics integration with economic models
Multi-series correlation analysis tools
Automated report generation from economic data
Important Disclaimer: FRED Economic Data Connector provides access to economic and financial data for informational purposes. The data and analysis generated by this tool should not be considered as professional financial, investment, or trading advice. All investment decisions should be made based on your own research, risk tolerance, and consultation with qualified financial professionals. Economic data may be revised historically. Use this tool at your own risk and responsibility.
Federal Reserve Economic Data (FRED) is the premier source for US economic time series data. Whether you're building economic models, monitoring market conditions, or conducting financial research, FRED connector provides the economic intelligence you need to make informed decisions.
Questions about implementing FRED connector? Our support team is here to help you integrate economic data into your workflows! 📊💹📈
Introducing the AI Classifier worker - a powerful new addition to the ApudFlow platform that brings intelligent decision-making capabilities to your workflows. Using advanced AI models, this worker can analyze complex data patterns and make classification decisions that drive your automated processes.
The AI Classifier worker leverages large language models to analyze data and classify it according to your specific instructions. Unlike traditional rule-based classifiers, AI Classifier can understand context, recognize patterns, and make nuanced decisions based on natural language prompts.
A quantitative trading firm implemented AI Classifier to automatically categorize incoming market data and route it to specialized analysis engines, reducing manual classification time by 95%.
An investment bank uses AI Classifier to assess risk levels of new positions in real-time, ensuring compliance with regulatory requirements and internal risk policies.
A hedge fund employs AI Classifier to analyze breaking financial news and automatically adjust portfolio positions based on sentiment and impact analysis.
Important Disclaimer: AI Classifier is a tool for automated decision-making and classification. The classifications and decisions generated by this tool should not be considered as professional financial, investment, or trading advice. All investment decisions should be made based on your own research, risk tolerance, and consultation with qualified financial professionals. Past performance does not guarantee future results. Use this tool at your own risk and responsibility.
AI Classifier represents the next evolution in workflow automation, bringing AI-powered intelligence to decision-making processes. Whether you're building trading systems, risk management platforms, or automated data processing pipelines, AI Classifier provides the intelligent routing capabilities you need.
Have questions or need help implementing AI Classifier in your workflows? Reach out to our support team!
In an era of data abundance, the real challenge lies not in collecting information, but in extracting meaningful insights that drive intelligent decisions. Introducing the AI Data Analyzer worker - a sophisticated AI-powered tool that transforms raw data into actionable intelligence, trends, and strategic recommendations.
The AI Data Analyzer worker employs advanced machine learning algorithms to analyze datasets, identify patterns, detect anomalies, and generate data-driven insights. Unlike traditional analytics tools, AI Data Analyzer understands context, recognizes complex relationships, and provides human-like interpretation of data with actionable recommendations.
Key Findings: - Strong upward trend identified with 68% momentum strength - Support level established at $142.50, resistance at $158.20 - Volume confirms trend direction with increasing participation Trends & Patterns: - Primary trend: Bullish (confirmed by moving averages alignment) - Secondary trend: Short-term consolidation forming potential cup pattern - Seasonal pattern: Q4 typically shows 12-15% gains historically Anomalies: - Unusual volume spike on October 15th (2.3x average) - potential institutional accumulation - Price gap down on October 22nd requires monitoring for retest Recommendations: - Maintain long position with stop loss at $145.00 - Consider adding to position on pullbacks to support levels - Monitor volume patterns for continuation signals - Watch for breakout above $158.20 for accelerated gains Risks/Considerations: - Market volatility increased 23% in past 30 days - Earnings report due in 2 weeks could cause volatility - Broader market correlation at 0.78 - watch S&P 500 direction
Input Data: Transaction history, browsing patterns, demographic data
Analysis Type: General
Detail Level: Detailed
Example Analysis Output:
Key Findings: - Customer lifetime value increased 23% YoY - Churn rate decreased to 4.2% (industry average: 6.8%) - High-value segment shows 34% engagement increase Trends & Patterns: - Mobile app usage up 45% since last quarter - Weekend activity increased 28% vs weekdays - Age group 25-34 shows highest engagement (52% of transactions) - Subscription upgrades concentrated in Q4 Anomalies: - Sudden drop in engagement from enterprise segment (-15%) - Unusual spike in support tickets from single user group - Geographic shift in user acquisition patterns Recommendations: - Launch targeted mobile marketing campaign - Develop weekend-specific promotions - Create loyalty program for 25-34 demographic - Investigate enterprise segment concerns - Optimize Q4 upgrade incentives Risks/Considerations: - Privacy regulations impact data collection capabilities - Economic factors may affect high-value segment behavior - Competitive landscape changes could impact retention
AI Model: Specialized Llama 3.1 model optimized for analytical tasks
Data Processing: Handles structured and unstructured data up to 50MB
Analysis Speed: Real-time processing for most datasets
Output Format: Structured insights with clear recommendations
A systematic trading firm implemented AI Data Analyzer to process multi-asset market data, identifying trading opportunities 40% faster than traditional methods while reducing false signals by 60%.
A major bank uses AI Data Analyzer to monitor transaction patterns, successfully identifying and preventing $2.3M in potential fraudulent activities within the first year.
An investment firm employs AI Data Analyzer for portfolio optimization, achieving 2.1% annual outperformance vs benchmark through data-driven rebalancing decisions.
A financial technology company integrated AI Data Analyzer into their robo-advisor platform, improving client satisfaction scores by 35% through personalized, data-driven recommendations.
Ready to unlock the power of intelligent data analysis?
Access AI Data Analyzer in your ApudFlow workspace
Prepare your dataset in structured format
Start with sample analyses to understand capabilities
Integrate into decision workflows for enhanced intelligence
Monitor and refine analysis parameters based on results
Important Disclaimer: AI Data Analyzer is a tool for data analysis and pattern recognition. The insights and recommendations generated by this tool should not be considered as professional financial, investment, or trading advice. All investment decisions should be made based on your own research, risk tolerance, and consultation with qualified financial professionals. Past performance does not guarantee future results. Use this tool at your own risk and responsibility.
AI Data Analyzer represents the future of data-driven decision making, transforming overwhelming datasets into clear, actionable intelligence. Whether you're managing investments, detecting fraud, or optimizing business processes, AI Data Analyzer provides the analytical power you need to stay ahead.
In today's information-overloaded world, the ability to quickly distill large volumes of content into concise, actionable insights is invaluable. Introducing the AI Summarizer worker - a powerful new addition to the ApudFlow platform that uses advanced AI to condense text, reports, and data while preserving critical information.
The AI Summarizer worker leverages state-of-the-art language models to analyze and condense lengthy content into focused summaries. Unlike simple text truncation, AI Summarizer understands context, identifies key themes, and creates coherent summaries that capture the essence of the original material.
Input: 2-hour earnings call transcript (50,000 words)
Prompt:"Summarize management commentary on strategy, outlook, and key initiatives"
Example Output:
Management Commentary Summary: **Strategic Priorities:** - Accelerate digital transformation across all business units - Expand into adjacent markets through strategic acquisitions - Invest $2B in R&D for next-generation technologies **Financial Outlook:** - Q4 revenue guidance: $8.2-8.5B (consensus $8.1B) - FY2026 revenue growth target: 12-15% - Operating margin expansion to 25% by 2027 **Key Initiatives:** - Launch AI-powered customer service platform by Q2 2026 - Complete 3 strategic acquisitions in healthcare sector - Achieve carbon neutrality by 2030 across global operations
AI Model: Optimized Llama 3.1 model for summarization tasks
Processing: Handles documents up to 50,000 words
Output Formats: Clean text summaries with metadata
Performance: Sub-second processing for typical documents
A quantitative investment firm implemented AI Summarizer to process 200+ research reports daily, reducing analyst reading time by 75% while maintaining 98% accuracy in key insight extraction.
A financial news platform uses AI Summarizer to condense breaking news into 100-word summaries, enabling real-time distribution to 500,000+ subscribers.
A major bank's compliance team employs AI Summarizer to review regulatory filings and risk reports, identifying critical issues 60% faster than manual review.
Ready to transform your content processing workflow?
Access AI Summarizer in your ApudFlow workspace
Start with sample content to understand summarization quality
Experiment with different summary types for various use cases
Integrate into existing workflows for enhanced productivity
Important Disclaimer: AI Summarizer is a tool for content condensation and summarization. The summaries generated by this tool should not be considered as professional financial, investment, or trading advice. All investment decisions should be made based on your own research, risk tolerance, and consultation with qualified financial professionals. Past performance does not guarantee future results. Use this tool at your own risk and responsibility.
AI Summarizer represents the next evolution in content processing, enabling professionals to stay informed without being overwhelmed. Whether you're analyzing financial reports, monitoring market news, or processing research documents, AI Summarizer provides the intelligent condensation you need to make faster, better decisions.
Questions about implementing AI Summarizer? Our support team is here to help you optimize your content processing workflows! 🚀📊
Workflow synchronization just got a whole lot easier with our new Wait for Workers worker! This powerful addition to the ApudFlow platform allows you to coordinate parallel workflow branches and ensure operations run in the correct order.
The Wait for Workers worker monitors the execution status of other workers in your workflow and waits until all specified workers have completed their tasks. It's perfect for scenarios where you need to:
Simply connect workers to your Wait for Workers node, and it will automatically detect and monitor all connected workers. No manual configuration needed!
This new worker significantly simplifies workflow coordination and makes building complex, parallel processing pipelines much more intuitive. Try it out in your next workflow!
ApudFlow now supports MongoDB for document storage with built-in user isolation. Whether you're building user profiles, storing analytics data, or managing content, MongoDB workers provide flexible document storage with automatic security.
MongoDB is a document database that stores data in flexible, JSON-like documents. Unlike traditional relational databases, MongoDB allows you to store complex, nested data structures without predefined schemas.
Every document is automatically tagged with your user ID, ensuring complete data separation between users. No cross-tenant data leaks or security concerns.
Collect and analyze usage data, performance metrics, or business intelligence data. MongoDB's flexible schema makes it easy to evolve your data structure as needs change.
MongoDB excels at handling large volumes of data with fast read/write operations. The document model scales naturally as your data structure evolves, making it perfect for growing applications.
Start building with MongoDB today and unlock the power of flexible document storage in your workflows!
ApudFlow now includes Redis support for high-performance key-value storage. Perfect for caching, session management, and real-time data operations that require speed and reliability.
Redis is an in-memory data structure store used as a database, cache, and message broker. It provides sub-millisecond response times and supports various data structures like strings, hashes, lists, sets, and more.
Algorithmic Trading Excellence
Redis excels in algorithmic trading due to its lightning-fast access speeds and atomic operations. High-frequency trading strategies require microsecond response times for market data, order execution, and risk management. Redis enables real-time strategy execution, instant position updates, and rapid arbitrage detection across multiple exchanges. Its pub/sub capabilities allow for real-time signal broadcasting to multiple trading algorithms, while sorted sets efficiently manage order books and priority queues for trade execution.
In algorithmic trading, every millisecond counts. Redis's in-memory storage eliminates disk I/O bottlenecks, ensuring strategies can react instantly to market movements. Complex algorithms can maintain state across multiple timeframes, track position sizes with atomic increments, and implement sophisticated risk controls. The ability to expire keys automatically handles time-sensitive data like quotes and orders, while Redis clustering provides the scalability needed for high-volume trading operations. Whether you're running statistical arbitrage, market making, or momentum strategies, Redis provides the performance foundation that separates profitable algorithms from those that lag behind.
In Apudflow, open the flow step with the “Telegram Notify” worker and paste the number into the “Chat ID” field.
Click run/test — you should receive a message in Telegram.
Group chat IDs
Add the bot to the group. If you have the webhook enabled, send /id in the group. The bot will reply with the group chat_id (usually a negative number).
Channel IDs
Make the bot an Administrator of the channel first (required).
Public channel: you can use the @channel_username as Chat ID.
Private channel: use the numeric id starting with -100… (get it by sending /id after the webhook is active, or with a utility bot like @getidsbot).
That's it! Your Chat ID is now saved in your flow, and you can receive alerts from your automations.
Example: "EoD: Top± NVDA +4.1% / T -3.7% | PnL +0.9% | Positions 12"Tip: Attach a chart image with photoUrl for context (e.g., a MultiChart snapshot). Your text becomes the caption.
This guide shows how to use MultiChart to analyze financial markets faster and with more confidence. It covers what MultiChart can render, proven layer recipes, practical workflows, and why a single, layered view is a real edge.
MultiChart combines multiple visualization layers on a single time axis. You can stack price, indicators, alternative chart types, volume, heatmaps, and order‑flow insights in one view with interactive legend, tooltip, and last‑value chips.
Tooltip shows context‑aware values (e.g., OHLC, Bollinger M/U/L, Ichimoku T/K/A/B/Ch, Volume). Legend above the chart lets you toggle layers instantly; last‑value chips on the right give a quick read of the latest levels.
Prefer passing expressions that evaluate to a list of dictionaries. You can reference two built‑in scopes: vars and data. If your platform supports mustache, you can wrap the expression, e.g. {{ ... }}.
Tip: Ensure timestamps are Unix seconds. If your source is in milliseconds, convert with r['ts'] // 1000 in a list comprehension.
Or derive from closes on the fly (simple example, pseudo‑rolling):
rowsExpr: "(lambda rows: [ {'ts': rows[i]['ts'], 'middle': sum([r['c'] for r in rows[i-19:i+1]])/20, 'upper': (sum([r['c'] for r in rows[i-19:i+1]])/20) + 2*(((sum([(r['c']- (sum([q['c'] for q in rows[i-19:i+1]])/20))**2 for r in rows[i-19:i+1]])/20))**0.5), 'lower': (sum([r['c'] for r in rows[i-19:i+1]])/20) - 2*(((sum([(r['c']- (sum([q['c'] for q in rows[i-19:i+1]])/20))**2 for r in rows[i-19:i+1]])/20))**0.5) } for i in range(19, len(rows)) ])(vars['fetch_yahoo_n_v7is']['data'])"
slug: multichart-legend-top-tooltip
title: MultiChart — Top Legend and Tooltip
authors: system
date: 2025-10-14
tags: [charts, ux]
Two UX improvements that make MultiChart easier to work with:
Legend moved above the chart — quick layer visibility toggles in a single row. The chart reserves top space so nothing overlaps.
Tooltip on hover — vertical guide line and a box with the time and values for all visible layers at that moment (OHLC, Bollinger M/U/L, Ichimoku T/K/A/B/Ch, Volume, etc.).
This speeds up analysis and cross‑layer comparisons.
slug: multichart-ichimoku-heatmap
title: MultiChart — Ichimoku and Heatmap
authors: system
date: 2025-10-12
tags: [charts, indicators]
We added two powerful visualizations to MultiChart:
Ichimoku: Tenkan, Kijun, Span A/B (with cloud), Chikou. Default colors and readable cloud opacity. Tooltip shows T/K/A/B/Ch at the hovered time.
Heatmap: A band at the bottom (time × Y category). Color from a palette (inferno by default; also viridis, magma, plasma). Does not affect the price scale.
We also polished backend computations (series normalization, safe parsing of values and timestamps).
Planned: Heatmap Y‑axis labels and Ichimoku component toggles in the configurator.
Threaded replies now auto-focus the input after send and optimistically insert your draft while confirming backend persistence, smoothing rapid follow-up exchanges.
