Systemic Docs

Name	Type	Description
Price	floatarray	The price_data input represents the historical closing prices of an asset to be analyzed by the Daily Return indicator.

Name	Type	Description
DR	floatarray	The dr output provides the calculated daily return values as a percentage change from each price point to the previous one.

Overview

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.

Daily Return is essential for:

Assessing short-term price movements
Quantifying daily volatility
Comparing performance across assets
Building more complex indicators (e.g., volatility, Sharpe ratio, cumulative return)

For more, see Investopedia: Daily Return and Binance Academy: Volatility in Crypto.

Mathematical Formulation

The Daily Return is calculated as:

\text{Daily Return}_t = \left(\frac{\text{Price}_t - \text{Price}_{t-1}}{\text{Price}_{t-1}}\right) \times 100

Where:

$\text{Price}_t$ is the closing price of the asset at day $t$
$\text{Price}_{t-1}$ is the closing price of the asset at day $t-1$

Visualizing Daily Return

Calculation Flow

flowchart TD
    A[Price Data] --> B[Get Previous Day's Close]
    B --> C[Calculate Price Change]
    C --> D[Divide by Previous Close]
    D --> E[Multiply by 100]
    E --> F[Daily Return Output]

Daily Return Signal Workflow

sequenceDiagram
    participant Market
    participant DR
    participant Trader
    Market->>DR: New closing price
    DR->>DR: Calculate Daily Return
    DR->>Trader: Output return value
    Trader->>Market: Adjust position or hold

Missing Data

DR computes the percentage change over observed bars only and reindexes back to the full input grid:

Closed-market gaps (e.g. weekends) stay gaps, and bridge. A missing input slot produces None in the output — never a carried-forward (stale) return, never a sentinel like 0. The first observed bar after a gap is measured against the last observed price before it (e.g. Friday's close), so the Monday return is a real bridged change, not a discarded weekend delta.
First bar is None. The very first observed price has no predecessor, so its return is honestly undetermined — never fabricated as 0.0.
Zero-base slots are None. A return computed against a 0.0 prior price is mathematically undefined (division by zero); the module emits None rather than a laundered sentinel.
Live mode. DR returns exactly one value for a live request: the most recent slot's return, or None if that slot itself is a closed-market gap or its base is undeterminable.

Practical Use Cases

In Cryptocurrencies

Volatility Monitoring: Crypto assets are known for their high volatility. Daily Return helps traders and risk managers monitor and react to large price swings, which are more frequent in crypto than in traditional markets. See Binance Academy: Volatility in Crypto.
Performance Comparison: Daily Return allows for direct comparison of performance across different coins/tokens, regardless of their price levels.
Risk Management: By tracking the distribution of daily returns, traders can set stop-losses, position sizes, and risk limits tailored to the asset's volatility profile. For more, see Cointelegraph: Crypto Risk Management.
Strategy Development: Many algorithmic and quantitative trading strategies in crypto use daily returns as a core input for momentum, mean reversion, and volatility targeting models. See CoinDesk: Quant Strategies in Crypto.

In Securities

Trend and Volatility Analysis: Daily Return is used to analyze short-term trends and volatility in stocks and indices.
Portfolio Construction: Helps in calculating risk-adjusted returns and optimizing asset allocation.

Daily Return in Macroeconomic Context

Daily Return is often analyzed alongside macroeconomic indicators (e.g., CPI, VIX) to understand how broader economic events impact asset volatility and performance. In crypto, macro shocks (like inflation data releases or regulatory news) can trigger outsized daily returns due to the market's sensitivity and leverage.

flowchart TD
    A[Macro Event: CPI, Fed Decision, Regulation] --> B[Market Reaction]
    B --> C[Price Change in Crypto]
    C --> D[Calculate Daily Return]
    D --> E[Adjust Trading or Risk]

For more on macro impacts, see Investopedia: How Economic Indicators Affect Markets.

Advantages and Limitations

Advantages:

Simple and intuitive calculation
Provides daily insight into asset performance
Useful for volatility and risk management
Enables cross-asset comparison

Limitations:

Focuses on short-term movements; may not reflect long-term trends
Can be noisy, especially in highly volatile markets like crypto
Sensitive to outliers and price anomalies

Data Alignment in Systemic

In Systemic, the Daily Return module processes price data that may come from various sources with different sampling frequencies. Understanding how the system handles data alignment is essential for accurate results.

Frequency and Padding Behavior

When connecting price data from a data source (e.g., a stock or cryptocurrency node), Systemic automatically handles alignment:

Scenario	Behavior
Single source	Price data is processed directly with no padding
Multiple sources (same frequency)	Data is aligned by timestamp; shorter series are padded with their last value
Multiple sources (different frequencies)	Lower-frequency data is upsampled to match the highest frequency before processing

Example: If you connect BTC at 1-hour frequency (3 data points) and ETH at 15-minute frequency (12 data points), Systemic will:

Upsample BTC from 1h to 15m by repeating each value 4 times
Pad both series to equal length if needed
Calculate Daily Return on the aligned series

Practical Implications

Accuracy at lower frequencies: The Daily Return calculation is most meaningful on daily closing prices. Using intraday data will compute "period returns" rather than true daily returns.
Upsampling effects: Upsampled data contains repeated values, which may result in zero returns during interpolated periods.
Cross-asset comparisons: When comparing Daily Return across assets with different data frequencies, ensure the frequency context is understood to avoid misinterpretation.

Indicator Assessment

The following quadrant chart evaluates the Daily Return indicator across key dimensions for both traditional securities and cryptocurrencies:

%%{init: {"themeVariables": {"quadrant1Fill": "#e8f5e9", "quadrant2Fill": "#e3f2fd", "quadrant3Fill": "#fff3e0", "quadrant4Fill": "#fce4ec"}}}%%
quadrantChart
    title Daily Return Indicator Assessment
    x-axis Low Complexity --> High Complexity
    y-axis Low Popularity --> High Popularity
    quadrant-1 High Popularity & Complex
    quadrant-2 High Popularity & Simple
    quadrant-3 Low Popularity & Simple
    quadrant-4 Low Popularity & Complex
    DR - Stocks: [0.15, 0.88]
    DR - Crypto: [0.18, 0.92]

Dimension Breakdown

Dimension	Stocks	Crypto	Explanation
Popularity	9/10	9.5/10	Universal metric; foundational to nearly all quantitative analysis
Reliability	9/10	8/10	Highly reliable in stocks; crypto's 24/7 trading and gaps affect consistency
Complexity	1/10	1/10	Extremely simple calculation—requires only price data
Versatility	9/10	9/10	Applicable across all asset classes, timeframes, and strategies
Recent Interest	7/10	8.5/10	Stable long-term; renewed interest in crypto volatility analysis

Key Insight: Daily Return scores exceptionally high on popularity and versatility while maintaining minimal complexity. Both traditional and crypto markets position it in the "High Popularity & Simple" quadrant, making it an essential foundational indicator for any trading system.

References and Further Reading

Glossary

Daily Return (DR): The percentage change in an asset's closing price from one day to the next
Volatility: The degree of variation in an asset's price over time
Risk Management: Strategies to control potential losses in trading
Macro Indicator: Economic data such as inflation, interest rates, or regulatory events
Outlier: An unusually large or small value in a data set

This documentation is for educational purposes and should not be considered investment advice. Always consult multiple sources and consider your risk tolerance before trading.

Daily Return

Inputs

Outputs