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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

12 documents

WonderTrader

This document is a historical intraday dataset for the Dalian Commodity Exchange iron ore futures contract. Its rows report timestamped five-minute open, high, low, and close prices, along with volume, turnover, and open interest. The visible entries cover…

FuturesCommoditiesChina marketsBacktesting
WonderTrader

This script configures WtCtaOptimizer to run parameter searches for a Dual Thrust strategy on a China Financial Futures Exchange index futures contract. It fixes inputs such as bar count, bar interval, lookback days, and contract, then varies the strategy…

FuturesChina marketsBacktestingRisk management
WonderTrader

The example sketches a Python environment that connects a CTA backtesting engine to an agent-like training loop. It initializes a backtest, subscribes a strategy to five-minute bars, starts asynchronous execution, and advances the engine one step at a time.…

FuturesBacktestingMachine learning
WonderTrader

This Python data-feed example adapts RQData historical futures data for use with the Wt trading framework. It maps standardized instrument codes to RQData symbols, requests bar or tick records, renames fields, and converts rows into Wt bar and tick…

FuturesMarket microstructureExecution
WonderTrader

This overview catalogs Python examples for the WonderTrader framework, including CTA strategies, futures and stock backtests, futures arbitrage, optimization, reinforcement learning, and high-frequency trading. It names Dual Thrust as a sample strategy used…

FuturesEquitiesHigh-frequency tradingArbitrage
WonderTrader

This Wtpy CTA strategy calculates upper and lower intraday trigger levels from recent highs, closes, and lows, scaled by configurable parameters around the current bar's open. When flat, it enters long on an upper-level break and, for non-stock instruments,…

FuturesBreakoutTechnical indicatorsExecution
WonderTrader

This code describes a Dual Thrust style breakout strategy applied across a list of instruments. It calculates a reference range from prior bars using the highest high and close and the lowest low and close, then scales that range by separate parameters to…

FuturesBreakoutTechnical indicatorsExecution
WonderTrader

This strategy tests whether two price series can support a mean-reverting spread. It applies augmented Dickey–Fuller tests to each series and their first differences, then uses a linear regression to estimate a hedge ratio and intercept when the series…

Pairs tradingMean reversionStatisticsFutures
WonderTrader

This code implements a Dual Thrust breakout strategy for a trading platform. It calculates a prior-period range from the highest highs, highest closes, lowest lows, and lowest closes across a configurable number of bars. The strategy scales that range with…

FuturesEquitiesBreakoutTrend following
WonderTrader

This configuration example sets up a CTA environment for a trading engine, including references to commodity, contract, holiday, session, fee, filter, execution, parser, and trader configuration files. It selects a trading session template and configures…

FuturesRisk managementPosition sizing
WonderTrader

The document demonstrates a workflow for backtesting a Dual Thrust strategy and reviewing its performance with Pyfolio. It configures a CTA backtest engine, sets the date range and storage location, and creates a strategy instance with a futures contract,…

FuturesBreakoutBacktestingStatistics
WonderTrader

This example configures a genetic algorithm optimizer to search parameters for a Dual Thrust futures strategy. It shows how to define an objective from average winning and losing trade results, assign fixed and variable parameters, set a backtest environment…

FuturesBacktestingMachine learningStatistics