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知識圖書館

這裡收錄 Stratmill 研究代理對 AI 代理閱讀過的書籍、論文、文章與程式碼所寫的摘要與核心觀點。每個頁面都連結至原始資料。

Quant Q&A
20,364 份文件
SuperMind
12,226 份文件
OKX Learn
8,431 份文件
Strategy library
7,910 份文件
MQL5 code base
7,090 份文件
BigQuant
3,481 份文件
Bitget Academy
3,298 份文件
MQL5 articles
3,012 份文件
TradingView scripts
1,976 份文件
ProRealCode
1,507 份文件
Deribit Insights
1,232 份文件
Machine Learning for Trading
1,124 份文件
arXiv papers
1,033 份文件
Amberdata research
766 份文件
FMZ forum
682 份文件
FMZ digest
662 份文件
vn.py community
560 份文件
QuantInsti blog
511 份文件
Galaxy Research
340 份文件
QuantStart
246 份文件
Stratmill research code
219 份文件
Robot Wealth
195 份文件
NautilusTrader
191 份文件
Hummingbot docs
181 份文件
Paradigm research
175 份文件
Lumibot
164 份文件
Kraken Learn
163 份文件
量化課程圖書館
157 份文件
OctoBot
152 份文件
Cryptohopper blog
144 份文件
Systematic trading blog (Rob Carver)
132 份文件
Qlib
116 份文件
TqSdk
86 份文件
Quantpedia
86 份文件
Hyperliquid docs
79 份文件
Freqtrade
68 份文件
Hudson & Thames
62 份文件
Awesome Systematic Trading
61 份文件
backtrader
54 份文件
vn.py
50 份文件
Binance API docs
45 份文件
Quantopian 講座
45 份文件
FMZ guides
38 份文件
pysystemtrade
34 份文件
Freqtrade docs
32 份文件
quant-trading
31 份文件
FinRL
28 份文件
Zipline
22 份文件
FMZ live strategies
21 份文件
Jesse
17 份文件
pyfolio
16 份文件
Alphalens
14 份文件
WonderTrader
14 份文件
backtesting.py
11 份文件
Technical Analysis
9 份文件
QTPyLib
8 份文件
QuantRocket
7 份文件
Lumibot strategies
7 份文件
Awesome Quant
1 份文件

搜尋圖書館

1,124 份文件

Machine Learning for Trading

This notebook explains how searching across many factors or strategies inflates the apparent performance of the selected winner, even when individual estimates are computed correctly. A simulated set of noise factors illustrates that the largest information…

統計因子投資回測風險管理
Machine Learning for Trading

This case study compares sequence models on an S&P 500 equity and options feature panel. It pairs an LSTM, which carries gated state through a lookback window, with an N-Linear baseline that removes the latest level and fits a linear mapping. The baseline…

股票選擇權機器學習回測
Machine Learning for Trading

This notebook explains TimeGAN’s five-part architecture: an embedder and recovery network map between raw observations and latent space, a supervisor learns temporal transitions, and a generator and discriminator train adversarially. It describes staged…

機器學習股票統計回測
Machine Learning for Trading

This notebook demonstrates connecting a shared ETF momentum strategy interface to Alpaca, with a simulated offline path available by default. It explains how broker and market-data adapters can change while the strategy, order log, and shadow-mode wrapper…

股票動能交易執行風險管理
Machine Learning for Trading

This notebook compares gross and net Sharpe across strategy cost sweeps drawn from case-study backtests. For each deployed strategy configuration, it reads the assumed per-leg cost and rebalance cadence from recorded artifacts, interpolates Sharpe at the…

回測風險管理交易執行統計
Machine Learning for Trading

This record documents a multi-agent research run on whether the United States would enter a recession by the end of 2026 under a specified resolution rule based on consecutive quarterly GDP contractions or an NBER recession date. Three agents searched for…

統計美國市場多資產
Machine Learning for Trading

This notebook converts rolling ETF return windows into two-channel images for a convolutional neural network. The Gramian Angular Summation Field rescales values within each window, maps them to angles, and records trigonometric relationships between pairs…

機器學習統計股票回測
Machine Learning for Trading

This notebook explains why a generic target-weight risk overlay is unsuitable for the described S&P 500 short-straddle engine. The engine assigns fixed capital shares to weekly cohorts and normalizes weights within each cohort to sum to one. Scaling those…

選擇權風險管理回測
Machine Learning for Trading

This document develops features for studying whether crowded positioning in crypto perpetual futures predicts relative returns. It explains the funding payment’s direction, the role of the premium index, and why raw funding or premium values may need…

加密貨幣永續期貨市場微結構
Machine Learning for Trading

Risk-Premium PCA modifies the covariance matrix by adding a weighted outer product of the training mean-return vector. Its leading eigenvectors define static factor portfolios: ordinary PCA is recovered when the weight is zero, while positive weights tilt…

因子投資統計機器學習回測
Machine Learning for Trading

This notebook measures how a selected US firm-characteristics strategy’s validation performance changes as assumed commissions and slippage vary. It re-runs the same configuration across a declared cost grid, splitting each total charge evenly between…

股票回測交易執行風險管理
Machine Learning for Trading

This notebook describes how to produce predictions for an untouched holdout period after choosing a configuration on validation data. It resolves the selected configuration, rebuilds its training specification for the holdout, and derives the training window…

股票機器學習回測統計
Machine Learning for Trading

This configuration describes an intraday study of order-flow signals across a point-in-time NASDAQ-100 universe. It defines a cost-screened subset using liquidity information available before each evaluation period, specifies decision schedules for several…

股票美國市場市場微結構機器學習
Machine Learning for Trading

This notebook uses a Gaussian mixture model to group months by the joint behavior of nine factor and asset-class return series. It standardizes monthly observations, drops months with missing values, and compares candidate cluster counts using BIC, AIC, and…

因子投資機器學習統計多資產
Machine Learning for Trading

This notebook builds a forecasting agent that alternates between reasoning and actions in a ReAct loop. Its action space is deliberately small: the agent can search for evidence or submit a probability forecast. A shared two-method interface lets the loop…

機器學習市場情緒統計
Machine Learning for Trading

This notebook examines the ADIA Lab causal discovery challenge as a benchmark for learning patterns within a known simulator family. Its task supplies many synthetic datasets paired with known directed acyclic graphs, then evaluates models on classifying…

機器學習統計
Machine Learning for Trading

This notebook describes how to produce holdout predictions for a selected CME futures model configuration. The configuration is chosen using validation results, then refitted on data ending before the holdout window opens. A label buffer separates training…

期貨回測機器學習風險管理
Machine Learning for Trading

This notebook explains Tail-GAN, a generative adversarial approach for creating financial return scenarios while targeting value at risk (VaR) and expected shortfall (ES). It uses differentiable sorting so gradients can pass through quantile estimates, and…

機器學習風險管理波動率投資組合建構
Machine Learning for Trading

This feature-engineering notebook explains how slower information can condition faster trading signals through fundamentals, macroeconomic data, and calendar context. It outlines value ratios such as book-to-market and earnings yield, quality measures such…

因子投資股票統計機器學習
Machine Learning for Trading

This notebook describes how to produce holdout predictions for a selected NASDAQ-100 microstructure configuration after model and strategy choices have been made on validation folds. It reconstructs the selected training specification, derives a holdout fold…

股票市場微結構機器學習回測
Machine Learning for Trading

This guide introduces the public CFTC Commitment of Traders reports as a source of weekly futures positioning data. It distinguishes the Traders in Financial Futures report, which categorizes participants such as dealers, asset managers, and leveraged money,…

期貨大宗商品市場情緒回測
Machine Learning for Trading

This Python module describes methods for quantifying uncertainty in strategy backtests using daily out-of-sample returns. It combines stationary block bootstrap intervals for return-series statistics, a Sharpe standard error adjusted for autocorrelation and…

回測統計風險管理投資組合建構
Machine Learning for Trading

This notebook synthesizes registered validation results for five latent-factor estimators across a set of case studies. It first maps which estimators have results for each panel, then identifies the leading estimator by mean daily Spearman rank information…

機器學習統計因子投資回測
Machine Learning for Trading

This notebook explains how a volume participation limit constrains the quantity of a parent order that can fill during each market interval. A broker calculates the allowed slice from observed traded volume and carries any unfilled remainder forward. Lower…

交易執行市場微結構風險管理股票