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.
43 documents
This article introduces five technical indicators for assessing price trends, momentum, and volatility: moving averages, the Average Directional Index, Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. It distinguishes…
This article surveys a collection of blog posts for readers learning about algorithmic trading. The topics range from mathematical and statistical foundations to strategy families such as momentum, arbitrage, market making, and machine learning. It also…
The article describes trading ideas as hypotheses about how an asset may behave in particular circumstances, then suggests developing them through experience, research papers, forums, books, and learning from practitioners. It gives momentum research as an…
The article introduces Bitcoin’s transaction ledger, UTXO accounting, public nodes, and Proof of Work consensus. It explains how miners compete to find a valid nonce, how difficulty targets regulate block production, and how block rewards and transaction…
The article proposes evaluating automated strategies with two linked measures: win rate and the ratio of average winning to average losing trades. It defines expected edge as win probability times average win minus loss probability times average loss, and…
This article describes an introductory online course on momentum trading offered through B3’s education platform in partnership with QuantInsti. It presents the course as suitable for learners with basic Python knowledge and says the material covers…
This project describes an automated strategy that uses live EURUSD prices to generate signals for EURUSD, USDCHF, and XOM. A long signal occurs when EURUSD rises above the highest close of the prior five days; a short signal occurs below the lowest close.…
The article introduces Ethereum as a blockchain platform for running smart contracts and decentralized applications. It explains Ether and gas, the Ethereum Virtual Machine, and examples of applications in decentralized finance and autonomous organizations.…
The document explains the martingale idea through conditional expectation and a fair coin game, then applies it to trade sizing. A martingale trading approach increases exposure after losses, often by doubling position size, in the hope that a later gain…
This project describes a directional index options strategy that uses NIFTY daily candles and 15-day simple moving averages of highs and lows to generate long call or put signals. Entry rules combine the current candle’s position relative to the averages…
The document explains how to plot daily candlestick charts and describes a simple rule-based strategy using the previous three candles to decide whether to trade long or short on the fourth day. It outlines plotting market data for an example equity ETF,…
This broad primer surveys financial markets, trading styles, instruments, analysis methods, risk management, trading plans, psychology, algorithmic trading, regulation, ethics, portfolio management, and company financial statements. It distinguishes…
The article introduces quantitative trading as the use of mathematical and statistical analysis, commonly applied to price and volume data. It describes using tools such as moving averages, ARIMA, exponential smoothing, and neural networks to investigate…
The article examines how fixed and trailing stop-loss rules affect a strategy’s return distribution. Its central point is that stopped trades remain part of the results: a stop can cut large losses while also closing positions that might have recovered or…
The document explains the Relative Strength Index as a bounded momentum oscillator derived from recent gains and losses. Its manual calculation example separates price changes into gains and losses, computes an initial simple average, then smooths subsequent…
This project studies daily price and volume data for 20 Indian equities selected from sector indices, using observations from October 2010 through December 2018 and a short out-of-sample period in early 2019. It tests three approaches: a short signal based…
This guide explains autocovariance and autocorrelation as measures of how a time series relates to its own past values. Autocovariance retains the units and scale of the data, while autocorrelation standardizes the relationship by variance, making it bounded…
The article explains how Quadratic Discriminant Analysis (QDA) differs from Linear Discriminant Analysis (LDA), then applies QDA to an intraday momentum strategy using three-minute e-mini S&P 500 futures data. LDA assumes classes share a covariance matrix,…
This project outlines a market-neutral long-short portfolio framework that selects assets with cross-sectional momentum and compares equal weighting, inverse-volatility weighting, Hierarchical Risk Parity (HRP), and Hierarchical Equal Risk Contribution…
This interview traces a trader’s progression from executing commodity orders to coding trading systems and researching algorithmic strategies. The subject describes developing trend detection and momentum systems with position sizing, then building a…
In this interview, trader Nikolas Pareschi recounts his move from discretionary trading to algorithmic research and describes how he developed ideas through finance papers and statistical testing. He emphasizes that many indicators and methods fail…
The article lays out a sequence for moving a trading idea toward live use: form a hypothesis, code it, backtest it, test it forward, paper trade, and then consider live trading. It recommends obtaining reliable market data, since missing values and…
This project studies historical ETHBTC order book data from Poloniex and tests two ways of using order flow in simulated trading. The first estimates prices from aggregated bid and ask positions and uses those estimates to filter trades generated by a…
This project describes a short-term long strategy that looks for renewed strength during a low-trend-strength phase. It requires the 21-period EMA to be above the 42-period EMA, and the 42-period EMA above the 63-period EMA. ADX must be at or below 25 and…