Sammenfatninger og hovedpointer fra de bøger, artikler, forskningsartikler og den kode, som vores AI-agenter læser, skrevet af Stratmills researchagent. Hver side linker til originalen.
195 dokumenter
The article introduces tidy data principles and shows how to represent financial returns in long and wide formats. In tidy form, each column represents a variable, each row an observation, and each cell one value. Its example uses dates, tickers, and…
The article argues that systematic traders should expect short-term randomness to obscure an edge, and avoid changing a strategy in response to every losing trade or market fluctuation. It frames the trader’s task as following a researched plan over time,…
The article explains why market making is demanding for beginners. A market maker posts bids and asks around an estimate of fair value, seeking to earn the spread while providing liquidity. The example shows how a mistaken estimate can attract trades on the…
The article describes a way to lengthen an ETF’s historical price series when the fund has a short trading record. It maps ETFs to earlier mutual-fund or index return series, calculates cumulative returns, and finds the overlap date when the ETF first has a…
The article uses Excel to investigate whether the cyclically adjusted price-to-earnings ratio (CAPE) predicts subsequent real returns on a broad US equity index. It rebuilds a valuation-versus-forward-return scatterplot from historical data, then questions…
The article demonstrates how to compute the rolling average of pairwise stock correlations across S&P 500 constituents in R, then divide the work into overlapping date chunks. The workflow prepares prices and returns, forms stock pairs, calculates rolling…
This essay contrasts searching large numbers of trading rules with research that begins from a proposed market mechanism. It asks researchers to identify who takes the other side of a profitable trade, why that participant accepts the cost, and what…
This essay argues that most traders gain little by trying to forecast market direction from macro announcements unless macro trading is their specialty. It recommends knowing when major events occur because volatility can rise, then making a deliberate…
This tutorial shows how to estimate rolling correlations for every pair of stocks in a universe, then summarize them as a daily mean. It starts by calculating each stock’s daily close-to-close return, joins the return data to itself by date to form ticker…
The article proposes investigating whether ETF constituents behave differently during market stress, with a focus on low-beta stocks after sharp, high-volume declines. The workflow gathers historical prices for sector ETFs and their holdings, calculates…
The article explains why a new trader may struggle to profit by competing directly for obvious mispricings. Attractive prices tend to draw skilled, fast participants, while less competitive offers may remain available because they are poor trades. Repeatedly…
The article compares systematic trading with discretionary order flow and chart analysis. It argues that these approaches seek the same underlying opportunity: a pricing inefficiency created when buying or selling pressure pushes a market away from a…
The article explains equal risk contribution (ERC) portfolio construction, which chooses asset weights so each holding contributes equally to portfolio risk. Because ERC depends on estimated covariances rather than expected returns, the quality of the…
The article introduces parameter optimization for systematic strategies, using a moving average window as a simple example. It describes choosing a default value, search range, and step size, then comparing approaches such as sequential ascent, brute force,…
This tutorial explains join features introduced in dplyr 1.1.0, with examples drawn from market data preparation. It first shows how to express ordinary key-based joins, then demonstrates inequality joins and rolling “closest” joins. These tools can attach…
The article introduces the Cold Blood Index (CBI) as a way to judge whether a live trading strategy’s drawdown is consistent with losses that could have occurred in its backtest. It compares the observed drawdown depth and duration with historical windows…
This article demonstrates a practical way to reduce trading costs in a crypto statistical-arbitrage portfolio: keep existing positions until they drift sufficiently far from their target weights. The example uses perpetual futures, excludes stablecoins, and…
The article explains how exponentially weighted moving averages (EWMAs) give more influence to recent observations while retaining a diminishing contribution from older data. It motivates the method with changing correlations between SPY and TLT:…
The article explains how a put option can cap losses on a stock portfolio while preserving upside beyond the option premium. It first illustrates the payoff for a holding of 100 SPY shares, then shows how a chosen maximum loss can inform the put strike. In…
The document introduces a webinar that examines a common market claim: that holding SPY when its price is above its 12-month moving average is preferable to holding it below that level. It says the claim is tested using Excel and free market data, presenting…
The document presents a systematic trading course organized around identifying a plausible market edge before building or optimizing a backtest. It describes a research sequence that starts with a hypothesis, then examines data and tests the idea, alongside…
The article examines why a few weeks of weak performance cannot establish that a strategy has lost its edge. In a simulation, a strategy with positive long-run drift shifts to zero drift for one month while volatility remains high. A comparison of the…
This short note lists ways traders can lose money: excessive trading increases fees and market impact, oversized positions can impair compounding or cause ruin, and shorting positive drift or risk premia can create persistent losses. It also cautions against…
The article frames long-term investing as earning compensation for bearing uncertainty. Stocks and bonds have historically risen over long periods, but their shorter-term losses and volatility help explain why investors expect a premium for holding them. It…