FlashAlpha VS htm.java

• REST API
  Pull options analytics via simple HTTP requests. JSON responses, standard authentication, 15-second cache refresh.
• Python SDK
  pip install flashalpha. Wraps all endpoints in native Python methods. Get data in one line of code.
• Real-time data
  Analytics computed from live options data during market hours. Covers 6,000+ US equities and ETFs.
• Gamma exposure (GEX)
  Per-strike dealer gamma exposure, gamma flip level, call wall, put wall, and regime classification. The core signal for intraday support and resistance.
• Volatility surfaces SVI
  VI-calibrated implied volatility across strikes and expirations. 3D heatmaps, skew profiles, and term structure analysis.
• Options Greeks
  Full Black-Scholes Greeks through third order. Delta, gamma, theta, vega, rho, vanna, charm, volga computed server-side.

• Biologically Inspired Algorithms
  HTM.java is based on Hierarchical Temporal Memory (HTM) theory, which mimics the neocortex's structure, making it innovative and potentially powerful for certain types of machine learning tasks, especially anomaly detection and sequence prediction.
• Time Series Prediction
  HTM.java excels in time series prediction and anomaly detection, which can be valuable for applications like financial forecasting, network monitoring, and IoT sensor data analysis.
• Open Source
  Being an open-source project, HTM.java allows developers to freely use, modify, and contribute to the codebase, fostering community-driven development and innovation.
• Java Ecosystem Integration
  HTM.java is written in Java, which means it can be easily integrated with other Java-based systems and take advantage of the vast array of libraries and tools available in the Java ecosystem.
• Real-time Analytics
  The framework supports real-time data processing, making it suitable for applications that require immediate insights from streaming data.

Possible disadvantages

• Complexity
  The underlying principles of HTM theory can be difficult to grasp, which may be a barrier for new developers trying to learn and implement the algorithms.
• Limited Adoption
  Compared to more mainstream machine learning frameworks like TensorFlow or PyTorch, HTM.java has a smaller user base and community, potentially leading to fewer resources and community support.
• Performance
  HTM algorithms can be computationally intensive, which might be a concern for applications requiring high performance or low-latency processing, especially when compared to optimized deep learning frameworks.
• Niche Use-Cases
  The strengths of HTM.java are specific to particular problems like anomaly detection and sequence prediction, making it less versatile for a wide range of machine learning tasks in comparison to more general-purpose frameworks.
• Documentation and Tutorials
  The available documentation and tutorials for HTM.java might not be as comprehensive or beginner-friendly as those for more established frameworks, potentially increasing the learning curve.

• Volatility Risk Premium in 30 Lines of Python (And the 5 Mistakes That Wreck It)

  I built FlashAlpha for this. GET https://lab.flashalpha.com/v1/vrp/{symbol} returns ATM IV, four matched RV windows (5d, 10d, 20d, 30d) using Yang-Zhang, the VRP for each, rolling z-score and percentile, put-call directional... - Source: dev.to / 6 months ago

• Historical Options Data API for Backtesting — Replay GEX, VRP & Dealer Positioning at Any Minute Since 2018

  FlashAlpha's Historical API changes that. The contract is simple: every live analytics endpoint, replayable at any minute since 2018-04-16, returned in the same response shape. One query parameter — at — and you get what GEX, DEX, VEX,... - Source: dev.to / 7 months ago

Tracking htm.java since Mar 2021.