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195 dokumenttia
The article explains how to split SPY’s adjusted daily price data into overnight and intraday returns. It defines the overnight leg as holding from one day’s close to the next open, and the intraday leg as holding from the open to that day’s close. Adjusting…
The article presents a research philosophy for systematic trading centered on identifying genuine market mechanisms and combining modest opportunities. An edge should have an explanation for why another participant accepts the other side of the trade, such…
The article demonstrates a spreadsheet-based permutation test for assessing whether an observed market pattern could arise by chance. Its example examines whether Bitcoin returns are unusually high on Tuesdays: daily returns are randomly shuffled, grouped by…
The article builds intuition for option pricing by comparing expiration payoffs with possible underlying prices. Calls pay the amount by which the underlying finishes above the strike, while puts pay the amount by which it finishes below. Before expiration,…
The article examines practical limits of traditional market-neutral pairs trading. Each trade consumes capital on two legs, incurs spreads and commissions on both, and may use capital on a fairly valued leg even when the opportunity is concentrated in the…
The article introduces rolling and expanding windows through stock-price examples. A rolling window calculates a statistic, such as a mean, over a fixed number of recent observations. As each new observation arrives, the window advances and older data drops…
The volatility risk premium (VRP) is the tendency for option implied volatility to exceed the volatility that later occurs. The article explains this as compensation for bearing the risk of sharp volatility spikes, comparing option selling to insurance:…
This introductory article asks whether deep learning can be useful for market forecasting and outlines the practical work involved. A trading researcher must frame the prediction as a suitable task, scale inputs, choose a network structure, tune model and…
The article explains why covariance estimates matter for portfolio risk: pairwise asset covariances combine with portfolio weights to determine portfolio variance. Using adjusted-price returns for SPY, TLT, and GLD, it first compares rolling-window…
This review surveys research on selecting and trading equity pairs, comparing distance-based matching, cointegration, correlation, and other selection criteria. A common design forms candidate pairs over one period and trades them during a subsequent,…
The article explains how an autoregressive model predicts the next exchange-rate value from prior observations, then examines whether those predictions could support AUD/USD trades. It discusses partial autocorrelation across several sampling intervals, fits…
The article demonstrates how to estimate historical FX rollover payments using central bank policy rates, a broker charge, and currency conversion. It implements the calculations in both Zorro and Python. The long and short roll estimates depend on the…
The article describes Apache Beam as a framework for building a systematic trading data pipeline. Its outlined workflow collects data from APIs, stores it, transforms and enriches records, calculates features, loads results into an analytical database, and…
The article demonstrates how to retrieve daily stock prices and company financial data through Finnhub’s API, then organize the responses into data frames. It describes the range of available information, including price history, current and historical…
The article frames the cost of SPX options as a comparison between option-implied volatility and a forecast of future volatility. It suggests treating options as expensive when the forecast is well below the implied level, and cheap when the forecast is well…
This article explains how to combine overlapping pair spread signals to infer which individual stocks appear rich or cheap relative to peers. Each spread acts as a relative vote; aggregating votes across a network can help distinguish a likely outlier from a…
This walkthrough tests whether a stock’s unadjusted closing share price predicts its return over the following year. It describes preparing adjusted price data while retaining unadjusted closes, trading-volume information, and index membership, then sorting…
This article uses k-means clustering to group daily GBP/JPY candles according to their high, low, and close relative to the open. It examines whether particular candle clusters tend to follow one another and whether returns after each cluster differ. The…
This tutorial builds an adaptive pairs trading example with gold and gold-mining ETF prices. A Kalman filter estimates a changing hedge ratio and intercept as new observations arrive. The prediction error is compared with its estimated standard deviation to…
The article presents a formula for the probability density of an asset’s future price under geometric Brownian motion (GBM), along with an R function that evaluates the density at a given price. Inputs include the starting price, per-step expected return,…
The article illustrates how a put option can limit downside on an equity holding and shows how the premium changes the portfolio’s payoff. It first models a position in an index-tracking fund, identifying the price level associated with a chosen loss and…
The article groups systematic strategies into three broad types, ordered by increasing turnover. Risk-premia harvesting seeks compensation for bearing risks that investors tend to avoid, using diversified exposure and sensible risk control; examples include…
The article explains how to assess candidate equity pairs and estimate a spread for mean-reversion trading. Using XOM and CVX as an example, it fits an ordinary least squares hedge ratio, forms a residual spread, and applies an Augmented Dickey-Fuller test.…
The article explains why doubling position size after each loss can make a losing strategy appear attractive until a sufficiently long loss streak causes severe losses or account ruin. It outlines a simulation using random trades and Martingale sizing, then…