references

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Table of Content

[] https://github.com/je-suis-tm/quant-trading [] https://github.com/wilsonfreitas/awesome-quant [x] https://www.tidy-finance.org/python/ [] https://github.com/shashankvemuri/Finance [x] https://github.com/stefan-jansen/machine-learning-for-trading [] https://wesmckinney.com/book/

Quantitative strategies are becoming a game-changer in binary options trading. Instead of relying on gut feelings, traders are applying data-driven models to identify high-probability setups with precision and discipline. By using quant models, decisions are based on numbers, patterns, and probabilities rather than emotions.

One approach is applying RSI (Relative Strength Index) in a systematic way. A quant model can test thousands of past signals to determine the most profitable thresholds for overbought and oversold levels, optimizing entry and exit points. Similarly, Bollinger Bands can be used in a statistical framework, where models calculate probabilities of price reverting back to the mean after touching the outer bands; giving traders clear, rule-based setups for short-term expiry trades.

More advanced traders also explore statistical arbitrage in binaries. This involves analyzing correlated assets—like EUR/USD and GBP/USD; and identifying temporary price inefficiencies. When correlations break down, a quant model can signal trades that profit from prices snapping back into alignment, even within short expiry windows.

By combining these quantitative tools, traders can move beyond guesswork and build strategies that are tested, repeatable, and data-backed. Quantitative trading in binary options may not eliminate risk, but it creates structure and consistency in a market where discipline makes all the difference.

[] speckit [] easyquanthttps://github.com/shidenggui/easyquant

Gemni report

你是一位专业播客博主，每天早上将为你的听众带去最新的财经、量化金融资讯，格式要求是：

1. 每一条报告必须包含标题，正文（摘要）和引用来源（链接）
2. 正文必须使用简洁的段落格式或项目符号列表，保持专业且易读的风格。
3. 每个类别可以有0到多条，总共返回10条数据
4. 每个 event 的发布时间必须是在最近48小时
5. 请使用中文

主题包含：

1. 量化交易（算法、策略、因子、数据、软件基础设施、模型，包括但不限于ython, numpy, pandas, polars, duckdb, clickhouse, postgresql, influxdb, redis, matplotlib, backtrader, vectorbot, statsmodels, scikit-learn, plotly, seaborn, ta-lib ）上的重大进展、更新、安全漏洞等事件
2. 重量级作者新发表的量化交易相关的会议、论文、报告、博客文章和最近一个月出版的书籍（但你不得重复推荐）
3. 强化学习、llm 在量化交易方面的最新研究、应用、案例分析等，特别是出现在https://www.alphaxiv.org/?sort=Hot中的
4. 最重要的财经新闻和观点（限2条以内，不要关注过于宏观的经济政策，聚焦在话题性比较强、关注度高的科技巨头公司和人物的消息上面），比如： a. 美股、欧洲、香港指数出现大于2%的显著波动 b. 如重要产品发布（含预告）、重大投资公告 c. 行业巨头如 Jensen Huang，Zuckerberg, larry Ellison, Lisa Su, 雷军，elon musk等人最新观点（采访、tweet 等） d. 重要投资人如 Aswath Damodaran, Cathie Wood、Citrini、MebFaber， Ray Dalio、段永平、但斌等（可扩展到资管规模50亿美金以上，或者社媒上10万粉丝以上的人物）人关于投资、财经、金融和量化的看法。
5. 主要对冲基金的重大新闻事件，特别是丑闻、重大技术变革、最新岗位、竞赛、招聘信息
6. reddit, x, facebook, instagram,linkedin, 雪球上 KOL（多于100k followers） 发表的关于投资的最新观点

排除：

1. 加密货币相关的新闻和观点
2. 个股的交易信号、交易策略收益等诱导性内容
3. 排除无法公开访问的内容，例如需要登录才能查看的内容