Quant finance blogs

本文介绍了多个专注于量化金融领域的博客资源,包括《TheWholeStreet》等聚合站点,以及一系列涵盖量化交易、统计套利、机器学习等内容的专业博客。这些资源对于希望深入了解量化金融实践和技术发展的读者非常有价值。

What I’ve learned from updating the blogroll.

New entries

The easy option is to go to The Whole Street which aggregates lots of quant finance blogs.

Somehow Bookstaber missed out being on the blogroll before — definitely an oversight.

Timely Portfolio was another that I was surprised wasn’t already there.

The R Trader talks about R in finance.

Math Trading seems to be pretty much like what you would expect from the title.

CSS Analytics: new concepts in quantitative research.

rbresearch: Quantitative research, trading strategy ideas, and backtesting for the FX and equity markets.

Gekko Quant is about: Quantitative Trading, Statistical Arbitrage, Machine Learning and Binary Options.

Dekalog Blog talks about statistics for developing a trading strategy.

Keplerian Finance is by a quant finance practitioner turned academic.

Butler’s Math: Applying mathematics to everyday life.

The Calculating Investor is for “those who want to do the math”.

Nerd’s Eye View is not all that nerdy as far as I can tell; it focusses on financial planning.

Eran Raviv talks about statistics, R, finance and so on.

The jury is out

Portfolio Heuristics is quite new but shows promise.

Adaptive Trader looks like it could be interesting.  However, there hasn’t been a new post since May and it was quite regular up until then.

Beyond the Blue Event Horizon is on hiatus but may be of interest.

Quantitative Research and Trading is another blog that has fallen silent.

Phorgy Phynance may or may not resume posting, and may or may not be of interest to you.

Alphaism has been inactive for about a year.

Summary

MoneyScience aggregates blogs in a complementary way to The Whole Street.  Between the two of them you can be very well informed.

If you are keen, then you can search the blogrolls of blogroll members.

It was about 3 years ago when I last seriously surveyed the quant finance blogosphere.  I find it remarkable how much more R is dealt with now relative to then.

### 关于 Quantitative Analysis 和 Quantum Computing 的关系 量化分析 (Quantitative Analysis) 是指运用统计学、数学以及计算机技术来研究金融市场的行为模式,从而帮助投资者制定投资策略。它通常依赖大量的数据处理能力和高效的算法设计[^1]。 量子计算作为一种新兴的信息处理方式,在理论上能够极大地提升复杂问题求解的速度和效率。特别是在涉及大量变量的优化问题中,比如组合优化或者路径规划等问题上,量子退火技术和变分量子算法展现出了显著的优势[^2]。因此,当把目光转向金融市场中的高频交易场景时,我们可以设想利用量子计算加速那些原本耗时较长的风险评估模型运算过程,进而提高整个系统的响应速度和服务质量。 然而值得注意的是,尽管目前已有不少关于如何将量子计算应用于实际业务流程的研究报道出来,但由于硬件设备成熟度不足等原因限制了其广泛应用的可能性;而且对于某些特定类型的数值模拟任务来说,传统高性能超级计算机仍然可能是更为现实的选择之一[^3]。 ```python import numpy as np from qiskit import Aer, execute from qiskit.aqua.algorithms import VQE from qiskit.aqua.components.optimizers import COBYLA from qiskit.circuit.library import TwoLocal def run_quantum_optimization(): backend = Aer.get_backend('statevector_simulator') optimizer = COBYLA(maxiter=500) var_form = TwoLocal(rotation_blocks='ry', entanglement_blocks='cz') vqe = VQE(var_form, optimizer) result = execute(vqe.construct_circuit(parameter_dict={}), backend).result() return result if __name__ == "__main__": res = run_quantum_optimization() print(res) ``` 以上代码片段展示了使用 Qiskit 库执行简单变分量子算法的过程,这正是探索未来可能用于改进现有量化交易平台性能的一种方法论实例。
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