Navigating the dynamic world of cryptocurrency perpetual contracts requires more than intuition—it demands data-driven decision-making. With the OKX market screener, traders gain powerful tools to filter, analyze, and visualize top-performing trading pairs based on real-time metrics like volume, volatility, and order size constraints. Whether you're a quant developer building algorithmic strategies or an active trader scouting high-opportunity markets, this guide breaks down how to leverage advanced screening techniques for optimal results on OKX.
How the OKX Market Screener Works
At its core, the market screener automates the process of filtering perpetual futures contracts using customizable thresholds. Built with Python and leveraging libraries such as pandas, plotly, and streamlit, the tool pulls live ticker data from OKX’s API, processes trading rules, and visualizes key performance indicators in an intuitive dashboard format.
The workflow includes:
- Fetching real-time tickers for all swap (perpetual) instruments
- Merging with contract sizes and minimum order requirements
- Normalizing price movements to compare volatility across assets
- Filtering markets by volume and minimum trade amount
👉 Discover how to identify high-volume, low-entry-barrier markets in seconds.
This enables users to focus only on tradable opportunities that meet their risk and capital parameters—removing noise and enhancing strategy accuracy.
Key Metrics Tracked by the Screener
To make informed decisions, the screener evaluates several critical metrics:
1. 24-Hour Trading Volume (Normalized)
High volume indicates strong market participation and better liquidity. The screener filters out low-volume pairs by allowing users to set a minimum threshold in millions of USD equivalent (volCcy24h).
2. Normalized Price Returns
By calculating high24h / low24h - 1, the tool measures intraday volatility. This normalization allows side-by-side comparison between stablecoins and high-volatility altcoins.
3. Minimum Order Amount
Calculated as min_order_size × last_price, this metric ensures traders don’t accidentally engage with contracts requiring larger capital than intended. It’s crucial for retail traders managing small portfolios.
4. Contract Size & Trading Rules
Each perpetual contract has specific constraints—minimum order size, price increments, and collateral tokens. These are pulled directly from OKX's trading rules endpoint and integrated into the analysis.
These insights empower traders to quickly spot assets with favorable conditions: high movement, strong volume, and accessible entry points.
Visualizing Opportunities: Interactive Charts
The screener generates two primary visualizations to enhance market understanding:
Normalized Returns Chart (OHLC Format)
This plot ranks trading pairs by their normalized price range over 24 hours. Using an OHLC (Open-High-Low-Close) structure scaled from the 24-hour low, it reveals which assets have exhibited the most significant upward or downward momentum.
Volume vs. Minimum Order Amount (Scatter Plot)
A logarithmic scatter plot compares daily volume against minimum trade size. Outliers in the top-left quadrant represent high-volume, low-access-barrier opportunities—ideal for scalpers and systematic traders.
These charts help users instantly assess trade viability without diving into spreadsheets or manual calculations.
Advanced Analysis: Drawdowns and Run-Ups Detection
Beyond surface-level screening, the tool performs deeper technical analysis using a peak detection algorithm based on a gamma threshold (e.g., 1%).
Here’s how it works:
- A new peak is registered when price rises above the prior peak by at least
gamma. - A drawdown begins when price falls below
peak × (1 - gamma). - The system logs timestamps, durations, and percentage changes between peaks.
This logic helps estimate:
- Maximum expected buy/sell spreads
- Average time between reversals
- Total potential trades within a period
Such metrics are invaluable for backtesting automated strategies or setting stop-loss/take-profit levels.
👉 See how peak analysis can refine your entry and exit timing.
Practical Use Cases
Case 1: Algorithmic Strategy Development
Quant teams use this screener to generate configuration files for bots like Hummingbot. By identifying pairs with consistent volatility patterns and known min/max order constraints, they ensure strategies operate within exchange limits.
Case 2: Risk-Aware Portfolio Expansion
Traders exploring new altcoin perpetuals can filter out illiquid or overly volatile markets. Setting filters like “minimum $100M volume” and “max order amount under $500” ensures safer exposure.
Case 3: Educational Visualization
For learning purposes, instructors can demonstrate market behavior across different asset classes—comparing BTC’s stability to speculative memecoins—using standardized normalized returns.
Frequently Asked Questions
Q: Can I use this screener without coding knowledge?
A: While the core implementation is code-based, platforms like Streamlit expose it via a web interface where users interact with sliders and dropdowns—no programming required.
Q: Is the data real-time?
A: Yes, the tool pulls live ticker and trading rule data directly from OKX’s public APIs, ensuring up-to-date market conditions.
Q: What time intervals are supported for candle data?
A: Users can select from multiple intervals including 1m, 5m, 15m, 1h, 4h, and 1d—ideal for both scalping and swing trading strategies.
Q: How does gamma affect peak detection?
A: Gamma acts as a sensitivity threshold. A lower gamma (e.g., 0.005) detects smaller fluctuations; a higher value (e.g., 0.02) ignores minor swings, focusing only on major moves.
Q: Can I export the filtered market list?
A: Yes, the tool supports saving session data via pickle serialization (backtesting_data.pkl), enabling offline analysis or reuse in other systems.
Optimize Your Trading Workflow Today
With increasing competition in crypto derivatives trading, edge comes from speed and precision. The OKX market screener delivers both—transforming raw data into actionable intelligence through smart filtering, visualization, and statistical modeling.
Whether you're refining a bot strategy or manually selecting your next trade, integrating structured screening practices elevates consistency and reduces emotional bias.
👉 Start analyzing high-potential OKX perpetual markets now—click here to explore live data insights.