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Knowledge library

Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.

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

Search the library

8 documents

Lumibot

This comparison explains how Lumibot and QuantConnect LEAN differ as algorithmic trading frameworks. Lumibot is presented as a Python-first library in which strategies use ordinary Python classes, broker and data adapters, and can combine deterministic rules…

BacktestingExecutionMachine learningStatistics
Lumibot

This document presents a QuantStats tear sheet for a strategy labeled “buffett-plain,” compared with SPY over January 4–15, 2026. It lists returns, drawdowns, risk-adjusted statistics, market exposure, daily outcomes, and two drawdown episodes. The reported…

EquitiesBacktestingStatistics
Lumibot

The document explains what LumiBot’s HTML backtest tear sheet and companion machine-readable metrics file contain. It lists return and risk measures such as annualized and total return, Sharpe and Sortino ratios, return over maximum drawdown, maximum…

BacktestingRisk managementPortfolio constructionStatistics
Lumibot

This document is a QuantStats tear sheet comparing an AI trading strategy with SPY over a brief January 2026 backtest, using Yahoo data. It reports a 1% total return for each, with the strategy showing a higher annualized return estimate but also a larger…

BacktestingStatisticsRisk managementEquities
Lumibot

The screen selects stocks whose daily high-low range exceeds a threshold, whose current high matches the highest high across the current and prior session, and whose closing price is below a specified level. The document gives equivalent indicator conditions…

EquitiesTechnical indicatorsStatistics
Lumibot

This document is a QuantStats performance tearsheet comparing a strategy labeled Ray Dalio Luna with SPY over the stated January 4–15, 2026 interval. It reports return and risk statistics, including a 1% total return for both, a 63.01% annualized return for…

BacktestingStatisticsRisk managementMulti-asset
Lumibot

The document describes a LumiBot strategy lifecycle hook for adding custom summary metrics to backtest tear sheets. It runs after trading has completed and strategy and benchmark returns and drawdown information have been prepared. A strategy can use the…

BacktestingStatisticsRisk management
Lumibot

This Chinese-language research summary studies stock-selection signals from large and small investor order flows. It reports that the two flows are negatively related and that normalized net flows have opposite associations with subsequent returns:…

EquitiesFactor investingStatisticsMarket microstructure