Alternatives
Products that do what Free open-source fx broker audit toolkit does
BEQI is an open-source Python toolkit
- 1TF
2011 · bullbear.ca
- 2IJ
Backstory: I wanted to play with intraday stock data but couldn't find a free dataset anywhere. IEXCloud [1] offers API access to 1-minute granularity intraday historical price data, but I was worried that it could get expensive or unwieldy to build up a substantial dataset via API calls. Plus, IEX gives out their raw data for free. I probably should have just used the IEXTools python library [2] to parse IEX's raw data dumps, but I was working on a Julia project, so it felt more thematically appropriate to build a new tool from scratch. I haven't been actively using InvestorsExchange.jl a…
2022 · github.com
- 3
- 4LW
Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Apr 2026 · github.com
- 5
- 6HF
2024 · github.com
- 7AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
- 8
- 9
- 10
- 11
Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
- 12
- 13FC
Finished my trading infrastructure project over the weekend - it’s called FLOX. Built in pure C++ (uses C++20 features), it’s a modular framework for building HFT systems, trading infra, market data feeds, or even something like a TradingView clone. Everything is open source: https://github.com/eeiaao/flox Docs: https://eeiaao.github.io/flox/ There are tests and benchmarks to keep it stable. Might add JS engine support later for scripting strategies. Open to feedback and contributions.
2025 · github.com
- 14
- 15TF
Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com
- 16TC
2019 · github.com
- 17
Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
- 18FF
finagg is a Python package and CLI that enables you to aggregate and update free financial data from a handful of high quality data sources all within your own SQL database. Here're some quick facts about it: - Implements nearly all of the BEA API, FRED API, and SEC EDGAR APIs (all of which have free and nearly unlimited data access) - Only focused on macrodata (no intraday data support) - Provides methods for transforming data from these APIs into normalized features that're readily useable for analysis, strategy development, and AI/ML - The most popular methods include getting…
2024 · github.com
- 19
- 20
AI stock research and multi-currency tracking for beginners.
Jul 2026 · insighthelix.app
- 21
- 22
Snap any Trading chart. Get instant AI technical analysis.
Jun 2026 · apps.apple.com
- 23
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →