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.
16,761 documents
This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…
This research review applies conditional generative adversarial networks (cGANs) to multi-asset allocation. It conditions the model on recent asset-return sequences and random inputs to generate possible future paths, then estimates expected returns and…
The document raises a position-management idea for an equity holding: after a stock gains 10%, sell half the shares to secure some of the unrealized profit while leaving the rest invested for further gains. Its example starts with 1,000 shares and proposes…
This research summary describes two strategies using constituents of the Shanghai 50. The momentum version ranks stocks by their return over the previous three trading days, selects the three strongest, weights them equally, and holds each cohort for nine…
The document describes an updated script for preparing data used by a trading strategy tester. It highlights three charting capabilities: building timeframes at any whole-second multiple, generating range bars with equal-height candles, and producing…
This document outlines an Expert Advisor that trades from signals generated by the NRTR_extr indicator. A signal is recognized when a large colored indicator dot appears at a bar’s close. The description identifies the required compiled indicator and notes a…
A VeighNa community exchange addresses two backtesting questions: whether multi-contract portfolio and CTA backtest modules expose account net value, and how to display or save CTA-style trade records and candlestick charts after a portfolio backtest. The…
This document describes an Expert Advisor that trades signals from the StochasticCGOscillator. It generates a signal when the indicator’s main and signal lines cross as a bar closes. The description explains the entry trigger but does not specify the…
This brief Chinese-language community exchange asks whether historical data can be queried in a quantitative research environment. A reply says that daily-frequency data can generally be requested by including a date in the natural-language query, and that…
The document is a strategy tester report for an automated trading system on spot gold, using an hourly chart over a stated historical period. Its parameter list includes fast and slow moving-average periods, ADX and RSI thresholds, and take-profit and…
The article describes a Uniswap V2 liquidity strategy that uses price volatility to decide when to provide or withdraw liquidity. It outlines the workflow: start with an even split of ETH and USDC, remove liquidity when volatility rises, rebalance the assets…
This weekly report compares stock selection portfolios built with several artificial intelligence methods across broad Chinese A-share and index-constituent universes. It separates results for unconstrained selection, industry-neutral portfolios, and…
This report evaluates momentum based sector allocation in Chinese equities. It compares ordinary lookback return ranking with risk-adjusted alternatives, including Sharpe ratio, information ratio, and a path-adjusted measure that divides return by the sum of…
This report summarizes quantitative approaches to enhancing returns over Chinese equity benchmarks while managing tracking error. It frames enhancement as a blend of active and passive investing and describes a top-down range of choices: market exposure,…
This Expert Advisor looks for a run of identical candlesticks and opens a buy after bullish candles or a sell after bearish candles. Its settings allow the user to choose the run length and trade size, add take-profit, stop-loss, and trailing-stop controls,…
This document describes a Chinese stock screen built around three filters: turnover between 3% and 12%, a positive but bounded 10-day price gain, and circulating market capitalization between 5 billion and 10 billion yuan. It presents the screen as a way to…
This document outlines a monthly long-only stock selection strategy for the Chinese A-share market. It ranks the universe using a market-cap rank and a 20-day average of price changes, combines the two into a score, then holds 50 equally weighted stocks with…
This research note explores additional technical factors for stock selection in the CSI 500 universe. It groups candidate measures into turnover behavior, illiquidity, and combined volume-price information. Turnover candidates include stability, coefficient…
The proposed Chinese stock screen combines a daily turnover range of 3% to 12%, an appearance on the previous day’s trading leaderboard, and large-order net flow above 0.05 for at least three consecutive days. The article frames these filters as measures of…
The document describes a MetaTrader script intended to identify times of day when an asset tends to move more. It proposes analyzing historical price movement across a chosen number of trading days, then presenting the results as a column chart that traders…
This example strategy searches Chinese A-shares for stocks that recently had a long run of consecutive limit-up sessions and then broke the streak. It further requires positive average returns over the three days after the break, excludes several board…
This stock-selection rule screens for shares with an amplitude above 1%, turnover above 2% and no more than 9%, and a 9:25 price gain below 6%. The stated rationale is to combine a measure of price movement with trading activity and a short-term opening…
The document reports a backtest of a long-only Simple Bollinger strategy on Binance ETH-USDT hourly candles from January 2019 through January 2021. Across 282 closed trades, the account grew from 10,000 to 31,030.44, with reported net profit of 21,030.4448,…
This article compares the data needs of crypto wallets, investors and traders, and decentralized applications. It explains that products may need blockchain balances, transaction histories, token transfers, contract events, and market prices or order books.…