Modules

A crypto ticker price feed on a given aggregation period.

U.S. interest-rate series — the full Treasury constant-maturity ladder, curve spreads, and the policy rate — for reading liquidity conditions and discount rates.

A practical guide to using CPI inflation and equity volatility to classify regimes, track the policy cycle, and inform crypto and equity trading.

A practical guide to using money and credit aggregates with policy plumbing to infer liquidity regimes and risk-asset performance.

Perpetual futures funding rate paid between longs and shorts. Positive = longs pay shorts, negative = shorts pay longs.

Forced position closures on perpetual futures, in the base asset (BTC for BTC/USDT, troy ounces for XAU/USDT). Long liquidations cluster on drops, short liquidations on rallies.

Aggregate trader positioning on perpetual futures. Long ratio = share of accounts net long; short ratio = share net short; their ratio summarises crowd bias.

A guide to using the open interest of Bybit's USDT perpetual futures, for crypto and for gold and silver, in trading and market analysis.

Absolute-dollar balance-sheet positions from SEC EDGAR quarterly filings, every one a snapshot as of the most recent quarter-end: assets, liabilities, inventory, long-term debt, and retained earnings.

Per-share SEC EDGAR quarterly figures — EPS, revenue per share, book value per share, share count, and dividends per share — the denominator side of every price ratio.

Absolute-dollar income-statement and cash-flow figures from SEC EDGAR quarterly filings, every one a trailing-twelve-month rollup: gross profit, operating income, net income, operating cash flow, and capital expenditures.

A practical guide to using SEC Form 4 insider buy/sell activity as a sentiment signal alongside a price series.

A practical guide to using Alpaca's US equity bars to price positions and build ratio-based signals alongside SEC EDGAR fundamentals.

A sweep asks a question many times at once. **Collapse** answers it once.

A sweep gives you many candidates. **Filter** decides which of them deserve your capital.

Every strategy contains a number you guessed. A moving-average window, a stop-loss percentage, a lookback for a z-score. You picked 20 because 20 felt reasonable, and never went back.

Many models — portfolio weight solvers, feature-importance charts, correlation grids — don't operate on a single series, they operate on a **table**: many series, read together at each point in time. **Make Matrix** builds that table from up to 20 series wired into its Row input.

The **Crossunder** module detects the precise moment when one data series crosses **below** another—a classic bearish signal in technical analysis. This is the mirror image of the Crossover module and is essential for identifying trend reversals and exit points.

The **Crossover** module detects the precise moment when one data series crosses **above** another—a classic bullish signal in technical analysis. This simple but powerful concept underpins some of the most widely-used trading strategies in the world.

The **Accumulation Distribution Index (ADI)**, also known as the Accumulation Distribution Line (ADL), is a cumulative volume-based indicator that reveals the flow of money into or out of an asset. Developed by Marc Chaikin, it combines price and volume to estimate buying and selling pressure—helping traders confirm trends, spot divergences, and identify potential reversals.

A practical guide to using the AROON indicator for identifying trend strength, timing reversals, and informing trading strategies in both traditional securities and cryptocurrencies.

A comprehensive guide to using the Average Directional Index (ADX) for measuring trend strength, filtering trades, and regime classification in equities and cryptocurrencies.

A comprehensive guide to using the Average True Range (ATR) for measuring volatility, setting risk levels, and informing trading strategies in both traditional and crypto markets.

A comprehensive guide to using the Awesome Oscillator (AO) to measure momentum, confirm trends, and spot reversals in equities and cryptocurrencies.

A practical guide to using Bollinger Bands for identifying volatility regimes, overbought/oversold conditions, and trading opportunities in both traditional and crypto markets.

A comprehensive guide to using the Chaikin Money Flow (CMF) indicator for measuring volume-weighted buying and selling pressure, confirming trends, and identifying reversals in both traditional and crypto markets.

A comprehensive guide to using the Commodity Channel Index (CCI) for identifying cycles, momentum extremes, and trading opportunities in both traditional and crypto markets.

A price can look like a turning point today and be surpassed tomorrow. **Confirmed Peak / Trough** waits for a chosen number of later values before calling a high or low a swing. It gives you two running reference levels: the last confirmed peak and the last confirmed trough. Connect closing prices to ask about turns in closes; connect an oscillator to ask a different question about that oscillator.

The **Constant Bias Scale** module applies a linear transformation to each value in a series—first multiplying by a scale factor, then adding a bias term.

A practical guide to using Cumulative Return (CR) for evaluating total investment performance, comparing strategies, and understanding compounding in both traditional and crypto markets.

The **Cumulative Volume Delta** module computes a running total of volume-weighted price changes, revealing the aggregate buying and selling pressure over time. This foundational volume indicator is widely used to identify divergences between price and volume—often a harbinger of trend reversals.

The **Daily Log Return** transforms price movements into the language of continuous compounding—the mathematical foundation of modern quantitative finance. While a simple return asks "what percentage did I gain?", the log return asks "at what continuous rate did my investment grow?"

The **Daily Return (DR)** indicator measures the percentage change in the closing price of an asset from one trading day to the next. It is a foundational metric in financial analysis, providing insight into an asset's day-to-day performance and volatility. Daily Return is widely used in both traditional markets (stocks, ETFs, indices) and cryptocurrencies, where it is especially valuable due to the high volatility and 24/7 trading nature of digital assets.

The **Detrended Price Oscillator (DPO)** is a technical analysis tool designed to remove long-term price trends, making it easier to identify short-term cycles and turning points. Unlike many oscillators, the DPO does not extend to the most recent price, instead focusing on past prices to highlight cyclical market behavior. This makes it particularly useful for traders seeking to isolate and analyze short-term price movements, independent of broader market trends.

The **Directional Movement Index (DMI)**, developed by J. Welles Wilder Jr., is a cornerstone of technical analysis for identifying the strength and direction of market trends. DMI is widely used in both traditional securities (stocks, ETFs, futures) and cryptocurrencies to distinguish trending from ranging markets and to generate actionable trading signals. It is composed of three main elements:

A practical guide to using the Donchian Channel (DC) for identifying breakouts, following trends, and measuring volatility in both traditional and crypto markets.

The **Ease of Movement (EMV)** is a volume-based oscillator that measures the relationship between an asset's price movement and its volume. EMV highlights how easily price moves up or down, factoring in both price change and trading volume. It is especially useful in fast-moving and high-volume markets like cryptocurrencies, where understanding the strength and sustainability of price trends is critical.

The **Exponential Moving Average (EMA)** is a widely used technical indicator that smooths price data by applying more weight to recent prices, making it more responsive to new information than the Simple Moving Average (SMA). This responsiveness makes EMA a favorite among traders and analysts for identifying trends, generating trading signals, and filtering market noise. EMAs are used across asset classes, including stocks, ETFs, and especially cryptocurrencies, where rapid price changes are common.

The **Force Index (FI)**, developed by Alexander Elder, is a momentum oscillator that measures the strength of buying and selling pressure by combining price movement and volume. It provides insight into the direction and intensity of market trends, as well as the likelihood of reversals. The Force Index is especially useful in fast-moving and high-volume markets like cryptocurrencies, where understanding the force behind price moves can help filter out noise and improve trading decisions.

Z = 1.2\frac{CA-CL}{TA} + 1.4\frac{RE}{TA} + 3.3\frac{EBIT}{TA} + 0.6\frac{MVE}{TL} + 1.0\frac{S}{TA}

Z'' = 6.56X_1 + 3.26X_2 + 6.72X_3 + 1.05X_4

\text{Graham Number} = \sqrt{22.5 \times EPS \times BVPS}

The **Piotroski F-Score** (Joseph Piotroski, 2000) grades a company's fundamental health on a **0–9 scale**, built from nine binary tests spanning profitability, leverage/liquidity, and operating efficiency — each comparing the current period against the same period one year (`year_lag` periods) earlier.

The **Ichimoku Kinko Hyo**, commonly known as the **Ichimoku Cloud**, is a comprehensive technical analysis system developed by Japanese journalist Goichi Hosoda in the late 1930s and published in 1969 after decades of refinement. Designed to provide a holistic view of market conditions at a glance, Ichimoku integrates trend, momentum, and support/resistance into a single chart. Its name translates to "one glance equilibrium chart," reflecting its goal of delivering actionable insights quickly. For a detailed history, see [Investopedia: Ichimoku Cloud](https://www.investopedia.com/terms/i/ichimoku-cloud.asp) and [TradingView: Ichimoku Cloud History](https://www.tradingview.com/wiki/Ichimoku_Cloud).

\text{KAMA}_t = \text{KAMA}_{t-1} + SC^2 \times (\text{Price}_t - \text{KAMA}_{t-1})

The **Keltner Channel (KC)** is a volatility-based technical indicator introduced by Chester W. Keltner in his 1960 book "How to Make Money in Commodities." The modern version, popularized by Linda Bradford Raschke, uses an Exponential Moving Average (EMA) as the middle line and bands based on the Average True Range (ATR). Keltner Channels are widely used in both traditional securities and cryptocurrencies to identify breakouts, overbought/oversold conditions, and trend direction. For more, see [Investopedia: Keltner Channel](https://www.investopedia.com/terms/k/keltnerchannel.asp), [StockCharts: Keltner Channel](https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels), and [Binance Academy: Volatility Indicators in Crypto](https://academy.binance.com/en/articles/volatility-indicators-in-crypto-trading).

The **Know Sure Thing (KST)** is a momentum oscillator developed by [Martin Pring](https://www.pring.com/) and first published in 1992 in *Stocks & Commodities* magazine. Originally called the "Summed Rate of Change," the KST was designed to solve a fundamental problem in technical analysis: **how to capture momentum across multiple time horizons in a single, actionable indicator**.

The **Mass Index** is a unique volatility-based indicator developed by Donald Dorsey in 1992. Unlike momentum indicators that measure *direction*, the Mass Index measures *range expansion and contraction*—detecting when volatility is building toward a potential trend reversal.

In the 1980s, J. Welles Wilder's RSI had become the go-to momentum oscillator. Traders everywhere used it. But two technical analysts—**Gene Quong and Avrum Soudack**—noticed something troubling.

The **MACD** (Moving Average Convergence Divergence) is one of the most widely recognized momentum indicators in technical analysis. Created by Gerald Appel in the late 1970s, it remains a cornerstone of trading systems because it elegantly combines **trend-following** and **momentum** analysis into a single measure.

The **Negative Volume Index (NVI)** is a cumulative indicator that tracks price changes exclusively on days when trading [volume](https://www.investopedia.com/terms/v/volume.asp) decreases from the previous period. Developed by **Paul Dysart in the 1930s** and later refined by **Norman Fosback**, NVI operates on a powerful premise: "smart money" (informed, institutional investors) tends to trade on low-volume days, while the "crowd" dominates high-volume sessions.

**On Balance Volume (OBV)** is a momentum indicator developed by **Joseph Granville** in 1963 and introduced in his book *Granville's New Key to Stock Market Profits*. The core thesis behind OBV is elegantly simple: **volume precedes price**.

Every trader faces a dilemma: how do you protect profits without exiting too early?

Here's a scenario that frustrated early technical analysts: You're comparing two stocks. Stock A ($500) has a MACD of 5. Stock B ($25) also has a MACD of 5. Which has stronger momentum?

Every price move carries a message, but volume reveals how many voices are behind it. A stock can rise 5% on 100,000 shares or 10 million shares — the price change is identical, but the conviction behind each move is radically different.

The **Rate of Change** indicator measures momentum by calculating the percentage change between the current price and the price $n$ periods ago. It's one of the simplest yet most effective momentum oscillators, answering the fundamental question: *"How fast is price moving?"*

The **Relative Strength Index (RSI)** is one of the most powerful and widely used momentum oscillators in technical analysis. Created by J. Welles Wilder Jr. and introduced in his seminal 1978 book *New Concepts in Technical Trading Systems*, the RSI measures the velocity and magnitude of price movements to identify overbought and oversold conditions.

In the 1990s, currency trader Doug Schaff faced a frustration familiar to every technical analyst: **the MACD was too slow.** By the time it confirmed a trend, much of the move had already passed.

In 1833, the Bank of England faced a delicate problem: how to report its bullion reserves without revealing the volatile daily fluctuations that might alarm the public. Their solution? Average the numbers over time. This technique—the **moving average**—was literally invented to smooth out an inconvenient truth.

In the late 1950s, George C. Lane made an observation that would become one of the most influential ideas in technical analysis:

RSI is one of the most popular indicators in trading. But it has a frustrating characteristic: **it rarely reaches extreme levels**.

The **Stock-to-Flow** ratio is a scarcity metric originally used to value precious metals, now widely applied to Bitcoin and other fixed-supply assets. It measures how many years of current production would be needed to match the existing supply.

In the early 1980s, traders using moving average crossover systems faced a brutal reality: **whipsaws**.

The **True Strength Index** is a momentum oscillator that shows both the direction and strength of a trend. It applies double smoothing to price changes to filter noise while remaining responsive.

The **Ulcer Index** measures downside volatility by quantifying the depth and duration of drawdowns. Unlike standard deviation, it focuses exclusively on price declines, making it a true measure of investment pain.

The **Ultimate Oscillator** combines buying pressure across three timeframes (7, 14, 28 periods) into a single oscillator. This multi-timeframe approach reduces false signals common in single-period oscillators.

**Volume Delta Divergence** measures the difference between buying pressure (ask volume) and selling pressure (bid volume), then identifies divergences between this delta and price movement.

The **Volume Price Trend** indicator measures the balance between a security's supply and demand by combining price movement with volume. Unlike simpler volume indicators, VPT weights each day's volume contribution by how much the price actually moved—giving you insight into whether large price swings are backed by conviction or whether small moves are hiding significant accumulation.

**VWAP** calculates the average price at which an asset has traded over a **rolling window of N bars**, weighted by volume — not the traditional session-reset VWAP. It continuously updates bar-by-bar, the same way an SMA does.

The **Vortex Indicator** detects the start of new trends or the continuation of existing ones. Developed by **Etienne Botes** and **Douglas Siepman** (published January 2010 in *Technical Analysis of Stocks & Commodities*), it draws inspiration from an unexpected source: the spiraling motion of water observed by Austrian naturalist **Viktor Schauberger**. Just as water forms vortex patterns flowing around obstacles, price movements in financial markets exhibit similar rotational dynamics between buyers and sellers.

The **Weighted Moving Average** addresses a fundamental limitation of the Simple Moving Average: treating all data points equally. In reality, recent prices are often more relevant to current market conditions than older ones. WMA solves this by assigning **linearly increasing weights** to each data point, with the most recent price receiving the highest weight.

**Williams %R** is a momentum oscillator that measures where the close sits relative to the high-low range over a lookback period. Developed by Larry Williams, it identifies overbought and oversold conditions.

Converts all values to their non-negative magnitude. Positive values stay positive, negative values become positive, zero remains zero.

\text{Sum}_i = A_i + B_i

The **Arccosine** module computes the inverse cosine of each value in a data series, returning the angle in **radians** whose cosine equals that value. This mathematical transformation has surprisingly powerful applications in finance—from analyzing correlation dynamics to building angular momentum indicators.

The **Arcsine** module computes the inverse sine of each value in a data series, returning the angle in **radians** whose sine equals that value. With its symmetric output centered on zero, arcsine is particularly powerful for analyzing oscillating indicators and extracting cycle phase information.

The **Arctangent** (arctan) module computes the inverse tangent of each value in a data series, returning the angle in **radians** whose tangent equals that value.

Does a market behave differently on the days you care about? **Conditional Mean** lets you ask that question of a numeric series. Give it **Values**, a true/false **Condition**, and a **Window**. It averages only the values whose condition is true. You can compare average trading volume on down days with volume on up days, or study returns in different volatility regimes.

Imagine holding shares in two different countries because their markets usually do not move in lockstep. During a difficult stretch, both begin falling together. Has the relationship changed, or are you seeing a few unusual observations? That is a question worth asking *before* you assume a second holding has diversified the first.

The Cosine module computes the cosine of each input value, interpreting them as angles measured in **radians**.

Computes element-wise division of two numeric series. Essential for ratios, normalization, and cross-asset analysis.

Floor Division divides two values and rounds down to the nearest integer—always toward negative infinity.

A price rises today—but compared with *which* earlier price? Yesterday's? The last available reading before a data gap? **Lag** brings an earlier numeric observation alongside the current one, so you can compare them in the rest of your system.

Computes element-wise multiplication of two numeric series. Essential for scaling, weighting, and position sizing.

Raises each element of the first input to the power of the corresponding element in the second. Essential for compound growth, volatility calculations, and non-linear scaling.

The **Sine** module computes the trigonometric sine of each value in the input series. This fundamental mathematical function maps angles (in radians) to their vertical projection on the unit circle, producing smoothly oscillating values bounded between −1 and +1.

Computes the principal square root of each value. A compression tool that reduces the scale of extreme values while preserving order.

Computes element-wise subtraction of two numeric series. Fundamental for spreads, price changes, and deviations.

The **Tangent** module computes the trigonometric tangent of each value in the input series. Unlike sine and cosine which are bounded, tangent produces **unbounded** values that represent the slope (rise over run) at any angle on the unit circle.

In the chaos of financial markets, **boundaries save portfolios**. The May 6, 2010 [Flash Crash](https://en.wikipedia.org/wiki/2010_flash_crash) saw the Dow Jones plunge nearly 1,000 points in minutes—a cascading failure partly caused by algorithms responding to extreme signals without limits. The **Clamp** module is your first line of defense: a simple yet powerful tool that enforces hard boundaries on any data series, preventing extreme values from propagating through your trading system.

\text{Result}_i =

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\text{Output}_i =

\text{Output}_i =

\text{Output}_i = A_i \land B_i

\text{Output}_i =

The **NOT** module inverts each boolean value in a series, transforming `true` to `false` and vice versa.

A \lor B =

The **Cumulative Average** module computes the running mean of a series—at each position, it returns the average of all values from the start up to that point.

The **Percentile** module computes a conditional quantile for each data point in a series. For every value, it filters the entire dataset to include only values less than or equal to the current value, then calculates the specified percentile of that filtered subset.

The **Standard Deviation** module computes the cumulative dispersion of data points from their evolving mean. Unlike a rolling window calculation, this module produces an *expanding* standard deviation—each output value reflects the variability of all data points from the beginning of the series up to that point.

The **Z-Score** measures how many standard deviations a value is from the mean. In trading, it quantifies how "unusual" the current price is relative to recent history, enabling mean-reversion strategies.

The **Z-Score** measures how many standard deviations a value is from the mean. In trading, it quantifies how "unusual" the current price is relative to recent history, enabling mean-reversion strategies.

The **Binary Classifier** module brings machine learning to your trading systems through an ensemble method called **AdaBoost** (Adaptive Boosting). It learns patterns from historical data to predict binary outcomes—such as "price goes up" or "price goes down"—and provides both a prediction and a confidence score.

> *"The best signal is not the one that predicts the market—it's the one that knows when it's confident enough to act."*

\hat{y} = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_n x_n