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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 evaluates NLinear, a simple sequence model that subtracts the latest observed level from a lookback window and maps the resulting sequence to a forecast with a linear transformation. It fits the ETF model population on walk-forward folds and…

股票機器學習統計回測
Machine Learning for Trading

This case study fits linear models to predict returns and direction in crypto perpetual futures. Its feature matrix is dominated by variations of the perpetual premium, the gap between the contract and spot price that drives funding payments. It contrasts…

加密貨幣永續期貨機器學習統計
Machine Learning for Trading

This notebook presents a conditional autoencoder for equity returns in which a neural network maps stock characteristics to nonlinear factor loadings, while another network extracts contemporaneous latent factors from characteristic-managed portfolio…

股票機器學習因子投資統計
Machine Learning for Trading

This notebook measures how transaction costs affect a fixed CME futures strategy configuration. It selects the reported configuration through a shared validation-stage process, carries its risk overlay into the cost runs, and applies a declared grid of…

期貨大宗商品回測交易執行
Machine Learning for Trading

This notebook explains how to rebuild a limit order book from DataBento market-by-order messages for a single NASDAQ symbol and trading day. It models each order, aggregates orders into price levels, and maintains separate bid and ask sides. Correct message…

股票市場微結構交易執行統計
Machine Learning for Trading

This notebook compares ways to hedge a short European call when rebalancing is discrete and trades incur proportional costs. It defines self-financing terminal P&L, then trains a neural hedger to minimize expected shortfall under Heston simulated prices. The…

選擇權機器學習風險管理交易執行
Machine Learning for Trading

This notebook compares six long-only ETF allocation methods designed to reduce reliance on unstable estimates. It applies Ledoit-Wolf covariance shrinkage to estimators that use covariance, and contrasts mean-variance maximum Sharpe with minimum variance,…

多資產股票固定收益投資組合建構
Machine Learning for Trading

This notebook compares single-objective hyperparameter tuning with a multiobjective search for LightGBM prediction models. The baseline maximizes validation information coefficient (IC). The NSGA-II search instead maximizes IC while minimizing normalized…

機器學習統計回測交易執行
Machine Learning for Trading

This chapter synthesizes nine case studies that take machine-learning signals through portfolio construction, trading costs, risk overlays, and frozen holdout evaluation. It treats each case study’s progression as the unit of analysis instead of ranking…

機器學習股票回測投資組合建構
Machine Learning for Trading

This assessment traces a single US equities strategy from a frozen validation backtest set to its holdout evaluation. It applies a deterministic rule: choose the candidate with the highest validation Sharpe, breaking ties by backtest hash. Registry records…

股票回測統計風險管理
Machine Learning for Trading

This notebook runs a selected NASDAQ-100 microstructure configuration on its registered holdout predictions. The model, allocator, concentration, rebalance schedule, risk overlay, and cost assumptions are inherited from earlier work and applied unchanged;…

股票美國市場回測風險管理
Machine Learning for Trading

This notebook compares ways to size positions in a US equities panel while holding the model, checkpoint, rebalance dates, and selected stocks fixed. Prediction-based methods scale capital by forecast magnitude or interval uncertainty; inverse volatility and…

股票美國市場投資組合建構部位規模
Machine Learning for Trading

This notebook explains how to select one strategy from a fixed set of US equity backtests and assess the resulting strategy on a separate holdout period. It validates that candidates share the required data and protocol identities, then ranks them by…

股票回測風險管理統計
Machine Learning for Trading

This notebook demonstrates an operational workflow for connecting a shared backtest and live strategy to an Interactive Brokers paper-trading session. It checks account identity and state, requests historical bars to initialize indicators, subscribes to…

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

This notebook compares three linear time-series models, a Transformer encoder, and parameter-free forecasts on daily SPY returns. The linear approaches map a historical window to a multi-day forecast, with variants that separate a smooth component from its…

股票機器學習統計回測
Machine Learning for Trading

This notebook presents a multi-round forecasting debate between bull and bear roles. Each side argues for a higher or lower probability of an event, sees the other side’s prior argument in later rounds, and reports a probability and supporting evidence. The…

機器學習統計市場情緒風險管理
Machine Learning for Trading

This notebook examines whether gradient-boosted trees can find nonlinear relationships in NASDAQ-100 microstructure features that a linear model may miss. It focuses on the possibility that order-flow imbalance predicts returns differently depending on the…

股票機器學習市場微結構統計
Machine Learning for Trading

This notebook explains an operator-style research agent for iterating on quantitative case studies. Instead of limiting the model to predefined forecasting actions, it gives it general tools for files, shell commands, registry queries, and data inspection.…

機器學習回測統計風險管理
Machine Learning for Trading

This feasibility analysis checks whether a daily long-short equity ranking strategy can be researched with the available US stock panel. It examines point-in-time universe construction using price and trailing turnover thresholds, compares proportional…

股票美國市場回測交易執行
Machine Learning for Trading

This notebook refits the configuration selected by earlier validation stages using pre-2021 history, then publishes predictions for a 2021 holdout. It retrieves the chosen configuration from a recorded candidate set or applies the same ranking rule when that…

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

This analysis introduces option-chain structure and examines a 2020 slice of S&P 500 options for eight underlyings. It explains moneyness, intrinsic and time value, Greeks, implied volatility, and the information represented by volatility skew and term…

選擇權波動率衍生品定價市場微結構
Machine Learning for Trading

This notebook implements an ESG headline workflow that selects news with keywords, assigns each selected headline to an environmental, social, or governance category, and scores sampled headlines with FinBERT sentiment. It measures the selected pool’s…

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

This notebook audits an annual-report corpus before it is indexed for financial research. It measures the delay between a fiscal period end and the public filing date, showing why point-in-time applications must use the publication date. It also identifies…

股票機器學習交易執行回測
Machine Learning for Trading

This notebook screens financial and model-based features for their relationship with the next eight-hour return of crypto perpetual contracts. For each settlement, it computes cross-sectional Spearman correlations across eligible contracts, then summarizes…

加密貨幣永續期貨機器學習統計