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.
5,018 documents
This research implementation turns a sparse linear model into a long-or-flat signal for GLD on the daily timeframe. The model uses five features derived from short moving averages of volume and price relationships among open, high, low, and close. It…
This indicator estimates whether current price action resembles the historical slope patterns associated with pivot highs or lows. It detects confirmed pivots, stores regression slopes around those events, and compares the current rolling close slope with…
This strategy combines two SuperTrend signals calculated on separate timeframes with a K-nearest neighbors classifier and directional filters. The KNN component uses nearby historical SuperTrend observations to classify trend direction through weighted…
This indicator uses two selected features, drawn from RSI, CCI, rate of change, and normalized volume, to compare current market conditions with stored historical observations. Each observation is paired with a label representing a subsequent price…
This strategy builds two SuperTrend lines from moving-average price estimates and ATR bands, then classifies trend direction with a weighted k-nearest-neighbors procedure. For each line, it stores recent SuperTrend values and labels them according to whether…
This educational indicator generates one simulated price-like path using a simple Markov process and random draws. Starting from a user-defined value, each step selects an upward move, a downward move, or no move; the first two outcomes have equal stated…
This Pine library collects functions for measuring distance or similarity between arrays and other representations. Its numeric-vector measures include squared difference, Euclidean, Manhattan, Minkowski, Chebyshev, correlation, cosine, Canberra, and…
This Pine Script library implements rolling regression tools for modeling a target series from as many as four predictors. Its methods include ordinary least squares, Ridge, Lasso, Elastic Net, logistic, robust Huber, and quantile regression. The…
This indicator estimates a near-term price move by comparing recent feature patterns with a rolling store of historical patterns. It represents each pattern with four inputs: one-bar log returns, five-bar price change, RSI centered around its midpoint, and…
Sniffer is a chart indicator that searches a configurable historical lookback for periods resembling a user-selected bar sequence. It represents bars with price and volume attributes, with optional features such as candle-body and wick ratios, rate of…
This Pine library implements ten kernel functions that map a distance from an origin and a bandwidth to a weight. It includes compact-support choices such as uniform, triangular, Epanechnikov, quartic, triweight, tricubic, and cosine kernels, alongside…
This indicator creates two or three regime centers for selected technical features using lower and upper percentiles together with a running mean. Each observation is assigned to its nearest center. In a combined mode, selected features can be standardized…
This indicator combines a multiscale high-pass filter with a simple adaptive binary classifier to produce a smoothed directional signal. The filter compares fast and slow components, reduces noise, adjusts the result for ATR, and normalizes it over a rolling…
This indicator constructs evolving support and resistance lines from price and intrabar volume data. Its design uses a particle-based local trend model and a fixed-lag smoother to estimate line slopes, while volume-weighted price samples inform how lines fit…
This EUR/USD strategy estimates linear regressions over short, medium, and long lookback windows. Each layer contributes a directional signal whose strength depends on slope, fit, correlation, and significance checks. It combines the layer signals using base…
This indicator applies a nearest-neighbor classification approach to market features, using a Lorentzian-style distance measure. Rather than summing raw feature differences as in Euclidean distance, its distance function sums logarithms of one plus the…
This indicator applies one-dimensional clustering to recent closing prices to identify price levels where observations concentrate. Users choose a cluster count, a training window, and either a mean-based or median-based center. Each close in the window is…
This Pine library provides approximations for the error function and its complement, their inverse functions, and related normal-distribution calculations. It exposes cumulative and complementary cumulative probabilities, inverse cumulative transforms that…
This strategy combines a VWMA and ATR based SuperTrend with a weighted k-nearest-neighbors classifier. The SuperTrend bands use the moving average as a center and adjust their distance according to volatility. The classifier compares recent SuperTrend values…
This indicator combines a double fractional-length exponential moving average measurement with two single-state Kalman filters, one baseline and one faster variant. Each filter updates its price estimate using a gain based on state uncertainty, process…
This educational indicator demonstrates a small feed-forward neural network that predicts the current close from the prior two closes. It normalizes prices by the maximum high observed so far, applies sigmoid activations in a two-unit hidden layer and a…
This indicator applies K-means clustering to a range of SuperTrend factor settings. For each factor, it tracks a performance measure based on whether the prior SuperTrend direction aligned with the latest price change. It groups those performance values into…
This indicator adapts a SuperTrend line to current volatility by clustering recent Average True Range values into high, medium, and low groups. It initializes three centroids from selected points within the ATR range, repeatedly assigns observations to their…
This indicator presents a simple classification approach that compares two selected inputs with past observations to produce a bullish or bearish reading. Inputs can include stochastic, RSI, z-score, money flow, volume, or distance from a moving average. The…