7 Best Meme Coin Trading Tools for 2026
Discover the 7 best meme coins trading platforms and tools for 2026. Get actionable insights, find winning wallets, and trade smarter on CEXs and DEXs.
February 20, 2026
Wallet Finder
February 20, 2026
Wallet trading frequency shows how often a wallet trades on the blockchain. It’s a great way to figure out if a wallet belongs to a daily trader, a weekly trader, or someone who trades rarely. This helps you understand their trading style and strategies.
Key steps to compare wallet trading frequency:
Tools like Wallet Finder.ai simplify this process by filtering data, calculating metrics, and providing alerts for sudden trading changes. Exporting data for further analysis allows deeper insights into trading behavior.
To get meaningful results from your analysis, it's crucial to start with clean and accurate data. Raw blockchain data can be a mix of simple transfers and more complex smart contract interactions. If you don't properly prepare it, irrelevant transactions might skew your findings.
First, define what you want to achieve. Are you tracking wallet activity, monitoring token movements, analyzing smart contracts, or studying broader transaction patterns? Your goals will determine which blockchain to focus on and the specific data you need.
For example, Ethereum is ideal for analyzing DeFi trading, while Bitcoin is better suited for traditional transaction studies. The tokens, wallets, or contracts you want to examine will guide your choice.
When collecting data, focus on transaction details such as addresses, amounts, fees, and timestamps. Wallet information - like balances, token holdings, and transaction history - can also provide valuable insights. If you're analyzing DeFi trading, include smart contract details like addresses, function calls, and their outcomes to deepen your understanding.
To ensure accuracy, use blockchain explorers to verify transaction data. Searching by wallet addresses or transaction IDs can confirm the details. Applying filters, such as specific wallet addresses, time frames, or transaction types, can help keep your dataset relevant and manageable. Once you've gathered accurate data, you can zero in on genuine trading activity.
Refining your dataset is the next step. Blockchain data often includes activities like simple transfers, staking rewards, airdrops, or failed transactions that don't represent actual trading. These can distort your calculations if left unchecked. Applying Gas Fee Optimization Tips for Blockchain Traders can help reduce costs and improve trade efficiency, ensuring that transaction expenses don’t erode your overall profits.
To clean up your data, focus on isolating transactions tied to exchanges, decentralized protocols, or swap functions. Exclude simple token or ETH transfers that aren't part of a trading protocol. Similarly, staking deposits and withdrawals, which typically indicate long-term holding strategies, should be set aside. By filtering out these non-trading activities, you can ensure your dataset reflects only active trading behavior.
Tools like Wallet Finder.ai can simplify this process by automating much of the data preparation. It filters out non-trading transactions and highlights genuine trading activity across various blockchains. This saves you from manually sorting through thousands of transactions and allows you to focus on analyzing trading frequency.
Export your cleaned data in formats like CSV for spreadsheets or JSON for programming. Make sure your dataset includes all timestamps and transaction details to support accurate frequency calculations.
Double-check that your dataset covers the intended time period and includes all relevant wallet addresses. Missing information could lead to misleading comparisons. It’s also a good idea to document your process: note the filters you applied, the time frames chosen, and how you defined trading versus non-trading activities. This documentation will help maintain consistency when updating or comparing data in the future.
Clean, structured data is the foundation for reliable analysis and meaningful insights.
With your refined dataset ready, here's how to analyze wallet trading frequency step by step. This method helps you uncover patterns and spot outliers among both active and occasional traders.
Start by defining your criteria. Are you focusing on the most active traders, those with consistent activity, or wallets showing a recent surge in trades? Your objective will guide which wallets to include in your analysis.
Use filtering tools to narrow down your options. Platforms like Wallet Finder.ai provide advanced filters, letting you sort wallets by factors like profitability, win streaks, and consistency. You can also filter by specific timeframes, minimum trade volumes, or tokens to create a more targeted comparison group.
For meaningful comparisons, group wallets with similar traits. For instance, compare wallets trading the same token pairs, working within similar price ranges, or achieving comparable profit levels. This ensures you're not comparing a high-frequency day trader with someone making occasional, long-term trades.
Aim to include 5 to 20 wallets in your group. Too few won't reveal enough trends, while too many could make the analysis harder to manage. Be sure to document the criteria you used to select each wallet.
Once your wallets are selected, it's time to measure their trading behaviors with clear metrics.
Start by calculating daily trades - divide the total number of trades by the number of days in your analysis. For instance, 150 trades over 30 days equals 5 trades per day. Expand this to weekly and monthly metrics for a broader view, which can help you spot consistently active wallets versus those with sporadic bursts of activity.
Go beyond the totals by measuring timing. Calculate the average interval between trades. For example, if a wallet completes 60 trades in 30 days, the average interval is about 12 hours. This helps distinguish traders who space out their activity from those who make rapid trades in short windows.
Group trades that happen within short time frames (like 1–2 hours) to identify trading sessions. This reveals whether a trader makes quick, successive trades or spreads activity over longer periods. Also, analyze peak trading hours to see when each wallet is most active.
With these metrics in hand, the next step is to bring them to life with visuals.
Turn your calculations into visuals to make patterns easier to understand. Here are some effective ways to present your data:
Add annotations for key market events to give context to your visuals. Use conditional formatting in tables - like color-coding cells based on activity levels (green for high, yellow for moderate, red for low) - to make important figures stand out.
For an extra layer of depth, consider interactive features. Hover-over details or dynamic filters allow users to explore the data further.
Keep your visuals clean and focused. Highlight the most critical insights, such as the highest and lowest frequency traders or significant changes in patterns, at the top of your summary. This ensures your findings are clear and actionable.
The article covers basic frequency calculations but lacks statistical rigor to determine whether observed differences between wallets represent meaningful patterns or random variation. Statistical significance testing provides the mathematical foundation to distinguish genuine trading behavior differences from noise in the data.
Hypothesis testing framework establishes whether frequency differences between wallets are statistically meaningful. Setting up null hypothesis (no difference in trading frequency) versus alternative hypothesis (significant difference exists) provides the structure for rigorous analysis. T-tests compare mean trading frequencies between two wallets, while ANOVA tests examine differences across multiple wallets simultaneously.
Sample size calculations determine how many trading days or transactions are needed to achieve statistical significance. Small sample sizes can lead to false conclusions about trading patterns. For reliable comparisons, most statistical tests require minimum 30 data points (trading days) per wallet, though more complex patterns may need 100+ observations for robust conclusions.
Confidence intervals around frequency measurements provide ranges of likely true values rather than point estimates. A wallet showing 5.2 trades per day might have a 95% confidence interval of 4.1 to 6.3 trades per day, indicating the uncertainty around the estimate. Overlapping confidence intervals suggest differences may not be statistically significant.
Power analysis determines the probability of detecting true differences when they exist. Low statistical power leads to Type II errors (missing real differences), while adequate power (typically 80% or higher) ensures reliable detection of meaningful frequency patterns.
Non-parametric tests handle situations where trading frequency data doesn't follow normal distributions, which is common in trading datasets.
Mann-Whitney U tests compare frequency rankings between two groups of wallets without assuming normal distributions. This approach works well when some wallets have extremely high or low trading frequencies that skew traditional statistical tests. Kruskal-Wallis tests extend this concept to compare multiple wallet groups simultaneously.
Bootstrap resampling generates confidence intervals and significance tests by repeatedly resampling from observed data. This technique works particularly well for trading frequency analysis because it doesn't require assumptions about underlying data distributions. Bootstrap confidence intervals often provide more accurate estimates for trading frequency comparisons than traditional methods.
Permutation tests create null distributions by randomly reassigning trading frequency values across wallets. This approach tests whether observed patterns could arise by chance alone. If less than 5% of random permutations show patterns as extreme as observed data, the result is statistically significant.
Effect size calculations measure the practical significance of frequency differences beyond statistical significance. Cohen's d quantifies how many standard deviations separate two groups' mean frequencies. Effect sizes above 0.8 indicate large practical differences, while values below 0.2 suggest minimal practical impact despite potential statistical significance.
Multiple testing problems arise when comparing many wallets simultaneously, inflating the chance of false discoveries.
Bonferroni corrections adjust significance thresholds when conducting multiple comparisons. For 10 wallet comparisons, each test requires p < 0.005 instead of p < 0.05 to maintain overall 5% error rate. This conservative approach reduces false positives but may miss true differences.
False Discovery Rate (FDR) control using Benjamini-Hochberg procedure provides less conservative multiple testing correction. This method controls the expected proportion of false discoveries among significant results, often more appropriate for exploratory wallet analysis where missing true patterns is costly.
Hierarchical testing structures multiple comparisons to maintain statistical validity. First test overall differences across all wallets, then conduct pairwise comparisons only if overall test is significant. This approach balances false positive control with statistical power.
Cross-validation techniques split trading frequency data into training and testing sets to validate statistical findings. Results that hold across different data subsets provide stronger evidence for genuine trading pattern differences.
Statistical rigor transforms trading frequency comparison from subjective pattern recognition into objective, mathematically-grounded analysis that supports confident decision-making about wallet trading behaviors.
This analysis goes beyond simple visuals to uncover deeper insights about trader behavior. By examining trading frequency data, you can better understand how different strategies influence wallet performance. Numbers alone don't tell the full story - they need context to reveal the decisions, strategies, and outcomes behind them.
Trading frequency plays a key role in identifying active traders and distinguishing between various trading approaches. Each strategy comes with its own characteristics and levels of risk, offering a window into the mindset and goals of different wallets.
High-frequency trading, often associated with strategies like scalping or day trading, focuses on taking advantage of quick, small price changes. Metrics like volume and speed can hint at methods such as market making, arbitrage, or directional trading.
On the other hand, lower trading frequencies typically align with strategies aimed at capturing long-term trends in the market. When combined with profit data, these patterns provide a clearer view of how different strategies impact wallet performance.
Looking at trading frequency alongside profitability data gives a fuller picture of how effective a wallet's strategy is. By analyzing both how frequently a wallet trades and the financial outcomes of those trades, you can identify which active traders are turning their strategies into consistent profits - and which are falling short.
Wallet Finder.ai simplifies this process with advanced filtering tools. These tools allow you to sort wallets by both trading frequency and profitability, helping you pinpoint wallets where active trading leads to meaningful, profit-driven results.
The article focuses on static frequency calculations but misses sophisticated temporal analysis that reveals how trading patterns evolve over time and respond to cyclical market conditions. Time series analysis uncovers seasonal trends, momentum patterns, and regime changes that static metrics cannot detect.
Autocorrelation analysis identifies whether today's trading frequency predicts tomorrow's activity. Positive autocorrelation suggests momentum in trading behavior - active days tend to follow other active days. Negative autocorrelation indicates mean reversion patterns where high activity periods alternate with quiet periods. Understanding these patterns helps predict future trading intensity.
Seasonal decomposition separates trading frequency into trend, seasonal, and irregular components. Many wallets show weekly seasonality with higher activity on weekdays compared to weekends. Some exhibit monthly patterns related to DeFi protocol reward distributions or traditional financial calendar effects. Holiday effects often reduce trading activity during major holidays across different regions.
Moving averages and exponential smoothing filter out short-term noise to reveal underlying trading frequency trends. Simple moving averages over 7 or 30 days smooth daily fluctuations, while exponentially weighted averages give more weight to recent activity. Comparing actual frequency to smoothed trends identifies periods of unusually high or low activity.
Change point detection algorithms identify when trading behavior fundamentally shifts. Structural breaks might occur when wallets change strategies, respond to market events, or begin following different trading protocols. CUSUM tests and Bayesian change point methods automatically detect these transitions in time series data.
ARIMA models (AutoRegressive Integrated Moving Average) capture complex temporal dependencies in trading frequency data.
Autoregressive components model how past trading frequencies influence current activity. AR(1) models examine whether yesterday's trading predicts today's frequency, while higher-order models AR(p) consider longer historical dependencies. Moving average components capture how past random fluctuations affect current trading patterns.
Integrated differencing handles non-stationary time series where trading frequency trends upward or downward over time. First differencing removes linear trends, while seasonal differencing removes recurring patterns. ADF tests (Augmented Dickey-Fuller) determine appropriate differencing levels for stationarity.
Seasonal ARIMA (SARIMA) models explicitly capture recurring patterns in trading activity. These models simultaneously handle within-week seasonality (weekday vs weekend patterns) and longer cycles such as monthly or quarterly patterns. Model selection uses AIC or BIC criteria to balance complexity with predictive accuracy.
GARCH models (Generalized Autoregressive Conditional Heteroskedasticity) model volatility clustering in trading frequency. These models recognize that periods of high trading activity variability tend to cluster together, similar to volatility patterns in asset prices. GARCH effects help predict not just average trading frequency but also uncertainty around those predictions.
Fourier analysis identifies dominant cycles in trading frequency data using spectral density estimation.
Periodogram analysis reveals which cycle lengths (daily, weekly, monthly) contribute most to trading frequency patterns. Peak frequencies in periodograms highlight dominant cycles, while spectral power measures cycle strength. This analysis helps distinguish meaningful cycles from random fluctuations.
Wavelet analysis examines how cycle patterns change over time, revealing non-stationary cyclical behavior. Unlike Fourier analysis which assumes fixed cycles, wavelets detect time-varying frequency patterns. This approach identifies periods when weekly trading cycles strengthen or weaken.
Regime switching models identify distinct states in trading behavior with different frequency characteristics. Markov switching models automatically detect transitions between high-activity and low-activity regimes. Hidden Markov Models infer unobservable market states that drive observable trading frequency patterns.
Cointegration analysis examines long-term relationships between multiple wallets' trading frequencies. Cointegrated wallet pairs maintain stable relative activity levels despite short-term fluctuations. Error correction models describe how wallets adjust trading frequency to maintain these long-term relationships.
Out-of-sample forecasting tests whether time series models can predict future trading activity accurately.
Rolling window validation trains models on historical data and tests predictions on subsequent periods. Walk-forward analysis continuously updates model parameters as new data becomes available. Forecast accuracy metrics like MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) quantify prediction quality.
Ensemble forecasting combines multiple time series models to improve prediction accuracy. Model averaging weights different approaches based on historical performance, while forecast combination techniques optimize weights to minimize prediction errors. Bootstrap aggregation generates multiple model variants to capture uncertainty in forecasts.
Leading indicator analysis identifies external variables that predict changes in trading frequency. Cross-correlation functions measure how market volatility, token prices, or social media activity relate to future trading patterns. Vector autoregression (VAR) models jointly analyze multiple time series to capture interactive effects.
Regime change prediction attempts to forecast transitions between different trading activity states. Probit models estimate probability of regime switches based on leading indicators. Early warning systems flag periods when trading behavior changes are likely, enabling proactive strategy adjustments.
Time series analysis reveals dynamic patterns in trading frequency that static calculations miss, providing deeper insights into how trading behavior evolves and responds to changing market conditions over time.
Once you've got the basics of comparing wallets down, advanced tools take monitoring to the next level. These tools can help you spot trading opportunities as they happen.
Advanced filters make it easier to pinpoint high-frequency wallets by narrowing down results with specific criteria.
For example, you can combine frequency filters with profitability metrics to identify wallets making more than 50 trades a week while still turning a profit. This method helps separate truly active traders from those who trade less frequently or with lower success.
Adding time-based parameters can refine your search even further. You could focus on wallets that are particularly active during certain market hours or those that stay consistent regardless of market conditions. This way, you can spot wallets that adjust their trading strategies based on market volatility.
Wallet Finder.ai makes this process seamless with its filtering tools. You can mix and match criteria to build custom filters tailored to your research goals. These custom screens allow for dynamic, real-time insights into wallet activity.
Real-time alerts make sure you don’t miss important shifts in wallet behavior. For instance, if a wallet that usually makes 5 trades a day suddenly jumps to 25, it might signal a new market opportunity or a change in strategy.
You can also set pattern change alerts to track longer-term shifts. These notifications highlight when a wallet’s trading behavior changes significantly over weeks or months, helping you uncover evolving strategies or responses to market trends.
Wallet Finder.ai integrates with Telegram, so you’ll get instant mobile notifications when something noteworthy happens. You can adjust the alert settings to suit your needs, whether you prefer immediate updates for big changes or daily summaries for a broader overview.
And if you want to dive deeper, exporting the data can open up even more possibilities.
Exporting raw data gives you the flexibility to perform custom analysis, backtesting, or integrate it with other research tools.
With CSV exports, you can easily work with structured data in spreadsheets or statistical software. These exports include key details like trading frequency, timestamps, transaction values, and profit metrics, giving you everything you need for detailed offline analysis.
If you're interested in long-term trends, historical data exports can be a game-changer. By compiling data over months or years, you can uncover seasonal patterns, correlations with market cycles, and how strategies evolve over time. These insights often go unnoticed in real-time monitoring.
You can also combine exported wallet data with external market data for deeper insights. For example, you might link trading frequency spikes to news events, new token launches, or periods of market volatility. This can help you understand what drives successful traders' decisions.
The exported data retains its full detail, including exact timestamps and transaction specifics. This level of precision is perfect for correlation studies or building statistical models to explore the link between trading frequency and profitability.
Some traders even use this data to create custom dashboards. By integrating Wallet Finder.ai data with business intelligence tools or other visualizations, you can build a personalized system that tracks not just wallet activity but also your portfolio performance and market indicators. This approach helps you turn raw data into actionable insights with ease.
Comparing wallet trading frequency plays a crucial role in spotting successful trading patterns and making sense of market behavior. By using the steps outlined in this guide, you can transform raw blockchain data into actionable insights.
The process starts with preparing accurate data and focusing on precise metrics. These steps are essential for meaningful wallet comparisons. Visual analysis makes it easier to detect patterns, whether it’s identifying trading spikes during volatile markets or finding wallets that remain consistently active no matter the conditions. The real advantage comes when you combine frequency data with profitability metrics. This approach helps you distinguish between high-volume traders and those who are truly successful.
Advanced monitoring tools add another layer of insight. Real-time alerts and filters can notify you of sudden shifts in trading behavior, helping you seize opportunities as they arise. Exporting data for deeper analysis also opens the door to backtesting and exploring correlations for a more detailed understanding.
Platforms like Wallet Finder.ai bring all these tools together in one place. With features like advanced filtering, Telegram alerts, and detailed data exports, it simplifies the process and integrates seamlessly with earlier analysis methods, creating a streamlined strategy.
Understanding trading frequency patterns gives you a clear edge in the DeFi world. By tracking high-performing wallets, you can uncover successful strategies and spot trends before they gain widespread attention. A systematic approach - from data preparation to real-time monitoring - lays the groundwork for effective wallet analysis. Consistency is key. Regular monitoring and analysis not only refine your long-term strategies but also deepen your understanding of market trends and trader behavior over time.
To get accurate and useful data for comparing wallet trading frequency, it's important to start with reliable data sources. Double-check their credibility and be on the lookout for any potential issues or gaps in the data.
Once you have your data, clean it up by removing duplicates, fixing any mistakes, and filtering out irrelevant transactions or unnecessary noise. This step ensures you're working with only the information that matters.
Next, organize the data in a clear and systematic way. Pay close attention to key details like transaction timestamps and amounts to make sure everything is accurate. When your data is well-structured and properly filtered, it becomes much easier to uncover active trading patterns and gain meaningful insights.
To get a clearer picture of wallet trading frequency and spot trends or patterns, visualization tools can be incredibly helpful. Tools like heatmaps, scatter plots, and time-series charts can showcase trading activity over time, making it easier to notice changes or repeated behaviors.
For digging deeper into the data, histograms and frequency charts work well to show trade distributions, highlight unusual activity, and reveal overall trading habits. These visual tools break down complex information, helping traders make smarter decisions and identify potential opportunities.
By looking at both how often trades are made and how profitable they are, you can better understand a wallet's trading strategy. It's not just about the number of trades - it’s about how successful those trades are. For instance, if a wallet shows frequent trades with strong profits, it likely reflects a smart and efficient strategy. On the other hand, frequent trades with low profits might signal higher risks or less effective choices.
This kind of analysis helps spot patterns that separate consistently profitable traders from those who struggle. It also sheds light on how trading behaviors might influence the market, giving a clearer view of how strategies connect to overall performance.
Statistical significance testing provides mathematical rigor to distinguish genuine trading pattern differences from random variation. Hypothesis testing framework compares mean trading frequencies between wallets using t-tests for two-wallet comparisons or ANOVA for multiple wallets. Meaningful analysis requires minimum 30 trading days per wallet, though complex patterns need 100+ observations. Confidence intervals around frequency measurements show uncertainty ranges - a wallet with 5.2 trades per day might have 95% confidence interval of 4.1 to 6.3 trades daily. Non-parametric tests like Mann-Whitney U tests handle non-normal trading frequency distributions common in real data. Bootstrap resampling generates confidence intervals without distribution assumptions by repeatedly resampling observed data. Effect size calculations using Cohen's d measure practical significance - values above 0.8 indicate large differences while below 0.2 suggest minimal practical impact. Multiple comparison corrections like Bonferroni or False Discovery Rate control prevent false conclusions when comparing many wallets simultaneously. Power analysis ensures adequate probability of detecting true differences when they exist, typically requiring 80% statistical power for reliable conclusions.
Time series analysis uncovers temporal patterns that static frequency calculations miss through sophisticated seasonal decomposition and cyclical analysis. Autocorrelation analysis identifies whether today's trading frequency predicts tomorrow's activity - positive autocorrelation suggests momentum patterns while negative indicates mean reversion. Seasonal decomposition separates trading into trend, seasonal, and irregular components, revealing weekly seasonality (higher weekday activity) and monthly patterns related to DeFi reward distributions. ARIMA models capture complex temporal dependencies through autoregressive components (how past frequencies influence current activity) and moving average components (how past fluctuations affect current patterns). Seasonal ARIMA models explicitly handle recurring patterns like weekday vs weekend differences. Fourier analysis identifies dominant cycles using periodogram analysis to reveal which cycle lengths contribute most to trading patterns. Wavelet analysis examines time-varying cycles, detecting when weekly trading patterns strengthen or weaken. Regime switching models automatically detect transitions between high-activity and low-activity periods using Markov switching frameworks. Change point detection algorithms like CUSUM tests identify when trading behavior fundamentally shifts due to strategy changes or market events. Cross-correlation functions measure how market volatility or external events relate to future trading frequency changes.
Machine learning clustering automatically identifies distinct trader types through sophisticated behavioral segmentation that manual analysis might miss. K-means clustering groups wallets with similar frequency characteristics using feature engineering that combines daily averages, peak activity periods, consistency measures, and volatility into comprehensive behavioral profiles. Hierarchical clustering creates nested groupings showing relationships between trader categories through dendrogram analysis - major divisions between high-frequency and low-frequency traders, then sub-categories within each group. Gaussian Mixture Models provide probabilistic assignment recognizing wallets may exhibit multiple trader type characteristics rather than hard categories. DBSCAN clustering identifies outlier wallets with unusual patterns that don't fit standard categories, useful for anomaly detection. Feature engineering creates sophisticated metrics like temporal consistency (coefficient of variation in daily frequency), activity burst analysis (intensity and recovery patterns), and market responsiveness (how frequency changes with volatility). Random Forest classifiers identify which frequency features best distinguish trader types through feature importance analysis. Support Vector Machines create optimal boundaries between categories in high-dimensional frequency space. Dynamic clustering tracks how trader types evolve over time using rolling window analysis and transition matrices quantifying behavioral category changes. Validation techniques like silhouette coefficients measure cluster quality while stability analysis ensures robust trader type identification.
Cross-market analysis reveals sophisticated relationships between individual trading frequency and broader market microstructure dynamics. Market volatility correlation shows how trading frequency responds to price volatility - high volatility periods often trigger increased activity as wallets respond to arbitrage opportunities and price movements. Correlation coefficients between volatility indices and aggregate frequency reveal market sensitivity patterns. Liquidity condition impact analyzes how frequency relates to market depth and bid-ask spreads - thin markets may reduce high-frequency trading due to slippage costs while deep liquidity enables more active strategies. Market stress testing reveals which trader types remain active during crisis periods, flash crashes, or regulatory events through regime-dependent analysis. Order book dynamics show how spread compression enables higher frequency trading by reducing transaction costs - tight spreads correlate with increased activity. Multi-factor models decompose frequency into systematic and idiosyncratic components using market beta coefficients to identify market-sensitive versus market-neutral approaches. Regime-switching models predict optimal frequency based on current market conditions - bull markets might favor higher frequency while bear markets suggest lower activity. Vector Error Correction Models analyze long-term relationships between market conditions and frequency patterns. Machine learning prediction combines volatility, liquidity, momentum, and sentiment indicators to forecast optimal frequency adjustments using Random Forest and Gradient Boosting models with feature importance analysis revealing which market conditions most strongly predict frequency changes.
A premier DeFi analytics platform empowering traders to discover and analyze profitable blockchain wallets, trades and tokens.
