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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 describes fitting PatchTST to one-minute NASDAQ-100 data to predict returns over several forward horizons. The model groups consecutive observations into patches and applies attention across them, reducing the number of items compared while…

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

This notebook checks whether historical ETF data can support a monthly ranking strategy before fitting a model or making forecasts. It tests the tradable universe using prior-year liquidity, counts eligible funds on rebalance dates, converts per-share…

多資產回測交易執行風險管理
Machine Learning for Trading

This notebook compares ways to allocate capital across US equity positions while holding the model, checkpoint, rebalance dates, and selected stocks fixed. It examines weights based on prediction strength, prediction-interval width, individual stock…

股票部位規模投資組合建構風險管理
Machine Learning for Trading

This notebook compares Polymarket’s crypto-settled event contracts with the regulated Kalshi venue as sources of alternative data. It explains how access rules, settlement assets, position limits, and listing policies shape which participants influence…

加密貨幣市場情緒統計事件驅動
Machine Learning for Trading

This notebook explains an unconditional signature-based Wasserstein GAN for generating financial time series. It transforms returns into augmented paths, computes truncated path signatures, and trains an LSTM generator driven by Brownian noise to match…

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

This dataset guide presents Binance perpetual futures price and volume data alongside an eight-hour premium index. Hourly OHLCV records describe market activity, while the premium measures the difference between perpetual and spot prices relative to spot. A…

加密貨幣永續期貨現貨市場Carry(套息)
Machine Learning for Trading

This notebook evaluates stop losses, trailing stops, and fixed-duration exits on selected US equity strategies. A stop loss responds to losses from entry, a trailing stop responds to declines from a position’s peak, and a time exit closes after a set holding…

股票風險管理回測部位規模
Machine Learning for Trading

This notebook demonstrates feature-drift checks on ETF momentum and technical features across calm and stressed windows, and on crypto perpetuals with premium-index data across market regimes. It compares Population Stability Index (PSI), including its…

機器學習統計風險管理加密貨幣
Machine Learning for Trading

This notebook evaluates TSMixer as a global sequence model for an ETF panel. The model shares parameters across funds while using each fund’s own history and covariates; its mixing layers learn temporal and within-series relationships without combining one…

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

This notebook defines forward-return targets for a cross-sectional futures strategy that ranks products by term structure, going long those with stronger carry and short those with weaker carry. It distinguishes roll-adjusted prices, appropriate for…

期貨大宗商品Carry(套息)統計
Machine Learning for Trading

This notebook explains how to interpret Kalshi’s federal funds rate contracts and prepare their prices for quantitative research. A binary contract price represents an implied event probability, but the feed contains the highest standing YES bid rather than…

固定收益統計市場微結構套利
Machine Learning for Trading

This notebook describes using TSMixer to predict stock returns from ordered windows of each stock’s features. Each example contains 60 consecutive sessions; windows that cross gaps in a stock’s history are excluded, so the number of usable examples varies…

股票美國市場機器學習回測
Machine Learning for Trading

This guide organizes US equity datasets into market data, company fundamentals, investor positioning, and firm characteristics. It inventories loaders for daily and intraday bars, options, and market microstructure records, as well as SEC filing text, XBRL…

股票美國市場市場微結構選擇權
Machine Learning for Trading

This notebook turns validation predictions from multiple model families into equal-weight, long-short portfolios. It ranks stocks by predicted return, buys the top names and shorts the bottom names, and sweeps several portfolio concentrations using a shared…

股票美國市場回測投資組合建構
Machine Learning for Trading

This notebook compares equity and futures commission models alongside several slippage models, emphasizing that their units and assumptions differ. Percentage fees stay constant as a share of notional, while minimums, fixed charges, per-share fees, and tier…

交易執行回測市場微結構風險管理
Machine Learning for Trading

This document describes training a Proximal Policy Optimization agent to liquidate a fixed order over a defined horizon. It evaluates the learned pacing policy against TWAP and an Almgren-Chriss schedule using the same simulated market paths, enabling paired…

加密貨幣交易執行市場微結構
Machine Learning for Trading

This document presents a staged process for linking company names from filings, news, and alternative data to securities. It distinguishes entity identifiers such as CIK and LEI from security identifiers such as FIGI, CUSIP, and ISIN, and explains why…

股票機器學習統計
Machine Learning for Trading

This document explains a supervised autoencoder factor model for predicting stock returns in an equity option analytics research setting. Unlike PCA, IPCA, and an unsupervised conditional autoencoder, its training objective combines reconstruction of the…

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

This document describes a fixed out-of-sample backtest for a selected CME futures strategy. The configuration, predictions, allocator, rebalance cadence, concentration, and transaction-cost assumptions are inherited from earlier research steps and applied…

期貨回測統計風險管理
Machine Learning for Trading

This notebook studies linear prediction models for a cross-section of CME futures products using feature columns grouped into related families, including carry, momentum, volatility, and rolling risk measures. Because columns within a family are often…

期貨機器學習因子投資統計
Machine Learning for Trading

This educational analysis compares four neural network designs on the same task: predicting the next daily return from a recent window of returns. A pooled set of eight exchange-traded funds provides varied market exposures. The models differ in how they…

股票機器學習回測風險管理
Machine Learning for Trading

This notebook audits the complete set of canonical validation predictions for an FX pairs study. It checks model identity, artifact availability, completeness, configured-label coverage, and whether the assembled configurations match the declared menu.…

外匯機器學習統計回測
Machine Learning for Trading

This notebook audits the registered validation predictions for an FX pairs study. It checks that the catalog includes the configured model population, complete prediction sets, available artifacts, and current model identities. It then summarizes predictive…

外匯機器學習統計回測
Machine Learning for Trading

This notebook measures how well the Lee-Ready method infers trade aggressor direction using Nasdaq order-by-order messages with venue-provided aggressor labels as ground truth. It reconstructs the limit order book from adds, modifications, cancellations,…

股票市場微結構交易執行統計