Skip to content

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

8,851 documents

QuantInsti blog

The project builds a long-only daily strategy across nine SPDR sector ETFs using short- and long-term exponential moving average crossovers. It reallocates capital quarterly based on each ETF’s relative quarterly return, aiming to direct more capital toward…

EquitiesTrend followingBacktestingPortfolio construction
QuantInsti blog

The document introduces price action trading as analysis of raw price movements and chart patterns, with limited reliance on indicators. It surveys support and resistance, trend following, candlestick setups, breakouts, range trading, and analysis across…

Technical indicatorsBreakoutTrend followingRisk management
QuantInsti blog

This article introduces technical indicators as mathematical transformations of price, volume, or related market data, and distinguishes technical analysis from fundamental valuation. It groups indicators into trend, momentum, volume, and volatility…

Technical indicatorsMomentumTrend followingBacktesting
QuantInsti blog

A bear trap is a brief selloff that appears to signal a reversal but is followed by recovery and continuation of an existing uptrend. The article describes how bearish traders may mistake a pullback or liquidation-driven move for a lasting breakdown, then…

ForexTrend followingRisk managementTechnical indicators
QuantInsti blog

The document describes a simple trend-following system built with linear regression. It introduces ordinary least squares and explains rolling regression as fitting a model repeatedly using a recent window of candles. In the example, the model relates each…

EquitiesMachine learningStatisticsTrend following
QuantInsti blog

The article presents a simplified Turtle-style strategy coded for equities. It enters long when the close exceeds a recent rolling high and short when it falls below a rolling low, then exits when price crosses a rolling mean. Signals are carried forward to…

EquitiesTrend followingBreakoutBacktesting
QuantInsti blog

This study compares two systematic futures trend approaches across indices, bonds, currencies, and commodities: a one-year time-series momentum rule and a continuous-forecast framework inspired by systematic trading methods. The first takes long or short…

FuturesTrend followingMomentumBreakout
QuantInsti blog

This overview describes momentum trading as taking positions in assets whose prices have recently moved strongly in a direction, on the premise that trends may persist before reversing. It distinguishes time-series momentum, which assesses an asset against…

MomentumTrend followingTechnical indicatorsRisk management
QuantInsti blog

The Parabolic Stop and Reverse indicator places a trailing value above or below price to indicate a possible trend direction and reversal. The article gives its update formula, which moves the prior SAR toward the trend’s extreme price using an acceleration…

Technical indicatorsTrend followingRisk managementBacktesting
QuantInsti blog

The article explains how the Average Directional Index (ADX) measures trend strength without indicating trend direction. Direction is assessed with the positive and negative directional indicators (+DI and -DI), while ADX is derived from the separation…

Technical indicatorsTrend followingStatistics
QuantInsti blog

The document explains the Ichimoku Cloud as a charting system built from five lines calculated from price highs, lows and closes over different lookback periods. Two leading spans form the cloud, while the other lines provide conversion, base and lagging…

Technical indicatorsMomentumCryptoTrend following
Hudson & Thames

The article describes an experiment applying meta-labeling to S&P 500 E-mini futures data. It combines event-based sampling, the triple-barrier method, and meta-labeling with two example strategies: trend following and mean reversion using Bollinger Bands.…

FuturesMachine learningTrend followingMean reversion
Hudson & Thames

This overview compares online portfolio momentum approaches across six equity and market-index datasets. Exponential Gradient updates portfolio weights using recent relative performance, with a learning rate and regularization intended to limit abrupt…

MomentumTrend followingEquitiesPortfolio construction
Hudson & Thames

The article presents Model Fingerprints as a way to describe how machine learning features affect predictions. It estimates partial dependence by varying one feature while averaging predictions over other observations, then separates that dependence into…

Machine learningStatisticsTrend followingTechnical indicators
Hudson & Thames

This project update describes research notebooks for financial machine learning topics, including tick, volume, and dollar bars; CUSUM event filtering; vertical barriers; and triple-barrier labels. It outlines comparisons of bar sampling using weekly count…

Machine learningStatisticsBacktestingMean reversion
MQL5 articles

The article presents a research workflow for turning a market hypothesis into a trading strategy. It uses block charts to examine whether price moves tend to continue rather than reverse, comparing observed movement distributions with a reference process…

Trend followingStatisticsExecutionBacktesting
MQL5 articles

The article derives price indicators from triangular and sawtooth window functions. It explains how combining moving-average coefficients produces a triangular weighting pattern, then extends the construction with multiple wave periods and separated…

Technical indicatorsTrend followingMean reversionRisk management
MQL5 articles

The article distinguishes genuinely adaptive indicators from filters that only appear to adapt. Averaging recent forecast errors changes the effective linear weights, but the resulting indicator is still a fixed linear combination of past prices. Laplace…

Technical indicatorsStatisticsTrend following
MQL5 articles

This article describes an MQL5 system that uses confirmed fractal pivots as price-structure anchors. It defines Break of Structure (BOS) as a closed-bar break beyond a previous swing high or low, and Change of Character (ChoCH) as an earlier warning that the…

Technical indicatorsBreakoutTrend followingBacktesting
MQL5 articles

The article tests how often markets qualify as trending versus flat by comparing five classification methods: ADX above a threshold, a Bollinger based trend indicator, Percentage of Trend, an RSI filter, and a ZigZag trend detector. It describes an MQL5 tool…

Technical indicatorsStatisticsTrend followingMulti-asset
MQL5 articles

The article explains how Renko charts represent price movement with fixed-size bricks while omitting regular time spacing. It outlines construction from a selected timeframe and box size, typically using closing prices: a new brick appears after price…

Technical indicatorsForexTrend following
MQL5 articles

The article presents Owl, a multi-indicator approach centered on trading in the direction of a broader trend. It distinguishes global trends from shorter corrective moves by comparing their scale and duration, and describes using swing highs and lows to…

Trend followingTechnical indicatorsRisk managementForex
MQL5 articles

This article outlines a MetaTrader 5 indicator framework that converts price bars into a structured account of recent market behavior. It detects confirmed swing highs and lows, enriches them with labels such as higher high or lower low, and records move…

Technical indicatorsTrend followingMomentum