The notebook is the primary workspace. If you have used Jupyter Notebook or Google Colab, the mental model is identical: a vertical stack of cells, each producing output, feeding into the next. But unlike Jupyter, these cells are purpose-built for trading analysis rather than general-purpose coding.
You start by adding a cell. Click the plus button, pick your cell type, and you are working. SQL cells open a CodeMirror editor with syntax highlighting, autocomplete, and schema awareness. It knows your table names and column names and suggests them as you type. Write your query, hit execute, and the results appear in a table below the cell.
From there, you link cells. A Visualization cell can reference the output of a SQL cell to render a chart. A Counter cell can pull a single metric from a query. Parameter cells create dropdown menus, date pickers, or text inputs that feed dynamic values into SQL cells using {{parameterName}} syntax. This means you can build a notebook that asks "which asset?" and "what date range?" and then runs your entire analysis pipeline with those inputs.
Cells can be collapsed, reordered, duplicated, and pinned to dashboards. Execution numbers track which cells have run and in what order, so you always know the state of your analysis. The system auto-saves every three seconds, so you never lose work.
Here is what a basic analysis notebook looks like in practice. You start with a Parameter cell that creates an asset selector defaulting to BTC. Then a SQL cell that pulls your recent trades for that asset:
SELECT
entry_date,
direction,
entry_price,
exit_price,
ROUND(pnl_percent, 2) AS pnl_pct,
ROUND(position_size, 2) AS size
FROM trades
WHERE symbol ILIKE '%' || {{asset}} || '%'
AND entry_date >= NOW() - INTERVAL '90 days'
ORDER BY entry_date DESC
| entry_date |
direction |
entry_price |
exit_price |
pnl_pct |
size |
| 2026-01-28 |
LONG |
102,450 |
104,890 |
2.38 |
5,000 |
| 2026-01-25 |
SHORT |
105,200 |
103,100 |
2.00 |
3,500 |
| 2026-01-22 |
LONG |
98,700 |
97,200 |
-1.52 |
4,000 |
| 2026-01-19 |
LONG |
96,300 |
99,800 |
3.63 |
5,000 |
| 2026-01-15 |
SHORT |
101,500 |
100,800 |
0.69 |
2,500 |
Then a Visualization cell linked to that query renders a bar chart of PnL by trade. Then a Counter cell shows your win rate. Then an AI cell interprets the pattern. Each cell builds on the last. That is the notebook flow.
The SQL engine is the backbone of the Workbench. Every other feature either generates SQL or consumes query results. And the data you can access is substantial.
Your personal data includes trades, balances, positions, watchlists, and alerts. Market data covers signals, events, divergences, sentiment data, liquidation events, smart money moves, funding rate history, and candlestick data across multiple timeframes. Workbench-specific data includes imported trades, strategies, backtests, custom signals, and datasets you have uploaded.
All queries are automatically scoped to your user account. You cannot accidentally see another trader's data, and you do not need to add WHERE clauses for user_id filtering. The system handles that transparently.
The editor provides real-time autocomplete that knows your schema. Start typing a table name and it suggests matches. Select a table and it shows available columns. This eliminates the need to memorize the data model.
Queries execute with a 10-second timeout and return a maximum of 1,000 rows. This keeps the interface responsive while still providing enough data for meaningful analysis. For larger datasets, you can use aggregations and filters to work with summaries rather than raw rows.
Results are exportable to CSV with one click. Query history is tracked automatically, so you can revisit and re-run past queries without retyping them. And every query goes through injection prevention, which only allows SELECT and WITH statements. You cannot accidentally (or intentionally) modify data through the Workbench.
Here is a query that finds assets where funding rates have diverged significantly from price action, a setup that frequently precedes mean reversion moves:
WITH recent_funding AS (
SELECT
symbol,
AVG(funding_rate) AS avg_funding,
STDDEV(funding_rate) AS funding_vol
FROM funding_rate_history
WHERE timestamp >= NOW() - INTERVAL '7 days'
GROUP BY symbol
),
price_change AS (
SELECT
symbol,
ROUND(((last_close - first_open) / first_open) * 100, 2) AS pct_change
FROM (
SELECT
symbol,
FIRST_VALUE(open) OVER (PARTITION BY symbol ORDER BY timestamp) AS first_open,
LAST_VALUE(close) OVER (PARTITION BY symbol ORDER BY timestamp
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_close
FROM workbench_candles
WHERE timeframe = '1d'
AND timestamp >= NOW() - INTERVAL '7 days'
) sub
GROUP BY symbol, pct_change
)
SELECT
rf.symbol,
ROUND(rf.avg_funding * 100, 4) AS avg_funding_pct,
pc.pct_change AS price_change_7d,
CASE
WHEN rf.avg_funding > 0 AND pc.pct_change < -5 THEN 'LONG SQUEEZE SETUP'
WHEN rf.avg_funding < 0 AND pc.pct_change > 5 THEN 'SHORT SQUEEZE SETUP'
ELSE 'NEUTRAL'
END AS signal
FROM recent_funding rf
JOIN price_change pc ON rf.symbol = pc.symbol
WHERE ABS(rf.avg_funding) > 0.01
ORDER BY ABS(rf.avg_funding) DESC
LIMIT 20
| symbol |
avg_funding_pct |
price_change_7d |
signal |
| DOGE/USDT |
0.0842 |
-8.34 |
LONG SQUEEZE SETUP |
| PEPE/USDT |
0.0631 |
-11.20 |
LONG SQUEEZE SETUP |
| SOL/USDT |
-0.0523 |
7.85 |
SHORT SQUEEZE SETUP |
| AVAX/USDT |
0.0412 |
-6.92 |
LONG SQUEEZE SETUP |
| ARB/USDT |
-0.0388 |
9.10 |
SHORT SQUEEZE SETUP |
That query, connected to a Visualization cell with a scatter plot, immediately shows you which assets have the highest squeeze potential. This kind of analysis would take hours of manual work across multiple platforms. In the Workbench, it takes 30 seconds.
Not everyone writes SQL. Not everyone should have to. The AI Chat mode lets you interact with the Workbench in plain English, and the system translates your questions into executable analysis.
You type a question or request. The AI Chat processes it and determines the best approach. It might generate and execute a SQL query, run one of the 15 quantitative analysis tools, build a visualization, or provide a direct analysis based on available data. The response streams in real-time, and you see the results as they generate.
The AI Chat supports four modes. Auto mode intelligently routes your query to the right approach based on complexity. Instant mode (15 credits) provides fast answers for simple questions. Deep Research mode (200 credits) runs comprehensive multi-step analysis for complex questions. Build mode generates execution plans for multi-step workflows that you can review before running.
The suggested prompts give you a taste of what is possible:
-
Trending: "What's ripping right now?" pulls real-time momentum data. "Top gainers with conviction" filters for moves backed by volume and on-chain activity, not just price spikes.
-
Find Alpha: "Find my alpha" analyzes your trade history to identify where your edge actually comes from. "What predicts my P&L?" runs correlation and regression analysis against your trading data. "Leading vs lagging signals" helps you figure out which signals to act on first.
-
Edge Health: "Is my edge degrading?" monitors whether your strategy is losing effectiveness over time. "How should I size?" runs position sizing optimization. "Optimize portfolio" analyzes risk-adjusted returns across your holdings.
-
Market Intel: "Smart money flows" pulls the latest whale activity. "Sentiment pulse" aggregates social and derivatives sentiment. "Divergence scan" finds assets where indicators disagree with price. "Derivatives overview" summarizes the futures and options landscape.
Here is what happens when you type "Which of my trades in the last 60 days had the best risk-adjusted returns, and what market conditions were they in?" into the AI Chat:
The AI generates a SQL query joining your trades table with market regime data, calculates Sharpe-like metrics per trade, identifies the regime at the time of each trade, and returns a formatted analysis. You see a table of your top 10 trades by risk-adjusted return, each tagged with the market regime (trending, ranging, or volatile), plus a summary explaining that your best trades cluster in trending markets and your worst trades happen in choppy, ranging conditions. It then suggests focusing your trading strategy on trending regimes and reducing size in ranging markets.
That entire analysis, from question to actionable insight, takes about 15 seconds. No SQL. No spreadsheets. No switching between tools.
The Workbench includes 15 purpose-built quantitative tools that cover the statistical tests and analyses professional quants rely on. These tools run either through the AI Chat (the AI decides which tool to use based on your question) or through dedicated Statistics cells in the notebook.
Calculates Pearson, Spearman, and rolling correlation between any two data series. Use this to find which signals actually move with your P&L, or which assets are diversifying your portfolio versus just adding noise.
Tests whether one time series has predictive power over another. This is the mathematical way to answer "does X lead Y?" without confusing correlation with causation. Critical for validating whether a signal actually leads price, or just moves at the same time.
Identifies pairs of assets that maintain a stable relationship over time, the foundation of pairs trading strategies. When two cointegrated assets diverge, they tend to revert, which creates a tradeable spread.
Classifies current and historical market conditions as trending, ranging, or volatile. Knowing the regime tells you which strategies to deploy and which to sit out. Most traders lose money by running trend-following strategies in ranging markets, and this tool prevents that.
Runs OLS regression with multiple independent variables to identify which factors explain your returns. Feature importance rankings tell you exactly which variables matter most.
Measures how quickly a signal loses its predictive power over different time horizons. A signal with rapid decay works for scalping. A signal with slow decay works for swing trading. Matching signal horizon to your holding period is essential for profitability.
Identifies outliers in any data series using z-score and IQR methods. Catches unusual market events, whale movements, and data quality issues before they corrupt your analysis.
Provides contextual interpretation of any signal, explaining what triggered it, the historical precedent, and the expected outcome based on similar past conditions.
Scans available data features to identify which ones have predictive power for your target variable. Instead of guessing which metrics matter, you let the data tell you.
Generates a composite conviction score from 0 to 100 for any signal based on multiple factors. Higher scores indicate stronger conviction, helping you filter out weak signals and focus on high-probability setups.
Combines multiple signals into an IC-weighted ensemble that outperforms any individual signal. This is how institutional quant funds build composite indicators, and now you can do it in three clicks.
Monitors your signals for degradation over time. Markets evolve, edges decay, and what worked six months ago might be crowded out. This tool catches the decay early so you can adapt before your returns evaporate.
Generates comprehensive reports covering alpha analysis, market regime breakdowns, signal health, and portfolio performance. These reports synthesize multiple analyses into a single document you can reference for decision-making.
Runs full strategy backtests with Monte Carlo simulation support. Covered in detail in the next section and in our complete backtesting guide.
Dissects your actual trading performance across multiple dimensions. Covered in the Trade Analysis section below.
The Strategy cell provides a no-code interface for defining trading strategies. You do not need to write Python, Pine Script, or any programming language. You select conditions from dropdown menus, set parameters, and the system translates your rules into an executable strategy.
Each strategy definition supports multiple entry conditions combined with AND/OR logic. You pick an indicator (RSI, MACD, Bollinger Bands, moving average crossovers, volume spikes, funding rate thresholds, and dozens more), set the condition (above, below, crosses above, crosses below), and specify the value.
Exit conditions work the same way. You define when to close a position based on indicator readings, time limits, or fixed targets. The system also supports stop loss, take profit, and trailing stop configurations with precise percentage or dollar amounts.
The Strategy Builder supports four position sizing models:
- Fixed Percentage: Allocate a constant percentage of capital per trade
- Kelly Criterion: Mathematically optimal sizing based on win rate and payoff ratio
- Volatility-Scaled: Size inversely to recent volatility so you take smaller positions in wild markets
- Risk Parity: Equalize risk contribution across positions
Strategies can include regime filters (only trade in trending markets), volatility filters (skip low-volatility periods), and time-based filters (avoid weekends or specific hours). These filters dramatically reduce false signals and keep you trading only in conditions where your strategy has an actual edge.
Here is how a mean reversion strategy looks when built in the Strategy cell:
- RSI(14) crosses below 30
- Bollinger Band Width(20, 2) is above 0.04
- Funding Rate is negative
- RSI(14) crosses above 55
- Position held for more than 48 hours
-
Stop Loss: 3% below entry
-
Take Profit: 6% above entry
-
Trailing Stop: 2% from peak
-
Position Sizing: Volatility-Scaled, 2% base risk
- Market Regime: Ranging or Volatile only
- Minimum Volume: $50M 24h
No code. No syntax to memorize. Select from dropdowns, click save, and move to the Backtest cell to see if the strategy actually works.
Building a strategy is step one. Proving it works is step two. The Backtest cell runs your strategy against historical data and gives you the statistical confidence to risk real money, or the evidence to go back to the drawing board.
Point your backtest at any strategy you have built and specify the asset, timeframe, and historical period. The system executes every trade the strategy would have taken and computes a full suite of performance metrics:
| Metric |
Value |
| Total Trades |
187 |
| Win Rate |
58.3% |
| Profit Factor |
1.84 |
| Sharpe Ratio |
2.12 |
| Sortino Ratio |
3.05 |
| Max Drawdown |
-12.4% |
| Calmar Ratio |
1.71 |
| Expectancy |
0.43R |
| Avg Win / Avg Loss |
1.32 |
| Total Return |
94.7% |
Beyond the summary stats, you get a full equity curve, drawdown chart, trade-by-trade log, and regime-segmented performance breakdown showing how your strategy performed in trending, ranging, and volatile conditions.
A single backtest tells you what happened. Monte Carlo simulation tells you what could happen. The system runs 500+ simulations by randomizing trade order and applying statistical variation. This produces a distribution of possible outcomes rather than a single path.
The output shows you the range of expected returns at different confidence levels. You can see the median outcome, the 5th percentile (bad luck scenario), and the 95th percentile (good luck scenario). If your strategy is profitable in the 5th percentile scenario, you have something worth trading. If it only works in the 95th percentile, your backtest results were driven by luck, not edge.
This is how institutional quant desks validate strategies, and it is built directly into the Workbench without needing to code anything.
Walk-forward analysis splits your historical data into in-sample (training) and out-of-sample (testing) windows, then walks forward through time, reoptimizing at each step. This tests whether your strategy adapts to changing market conditions or breaks down outside its training period.
A strategy that passes walk-forward analysis is dramatically more likely to perform in live markets than one that was only optimized on a single historical period. This is the difference between curve-fitting and finding genuine edge.
Simple backtests (single asset, under one year, no Monte Carlo) cost 30 credits. Complex backtests (multiple assets, over one year, Monte Carlo enabled) cost 50+ credits. The cost reflects the computational resources required for each simulation.
The Trade Analysis cell takes your actual trading history and breaks it apart across seven dimensions. This is not generic "you won 55% of your trades" reporting. It is granular performance attribution that tells you exactly where your money comes from and where it leaks.
-
Profitability Profile: Win rate, expectancy, average R-multiple, profit factor, and a strength/weakness matrix. Tells you whether you are a skilled trader or a lucky one.
-
Time Heatmap: Your P&L mapped to hour of day and day of week. Reveals when you trade best and when you should step away. Many traders discover they are consistently profitable in certain sessions and consistently negative in others.
-
Regime Performance: How your returns break down by market regime. If you are profitable in trends but bleeding in ranges, you know to reduce size or stop trading entirely when the regime shifts.
-
Edge Stability: Rolling win rate and expectancy plotted over time. A stable line means durable edge. A declining line means your edge is decaying and you need to adapt.
-
Setup Clustering: Groups your trades by setup type and shows performance per cluster. Maybe your breakout trades crush it but your mean reversion trades lose money. Now you know which setups to keep and which to drop.
-
Signal Correlation: Measures the correlation between the signals you follow and your winning trades. Tells you which signals actually predict your profitability and which are noise.
-
Emotional Bias: If you log emotional state with your trades, this analysis reveals how emotions affect your results. Revenge trades after losses, oversizing after wins, hesitation during drawdowns. The data does not lie.
The Visualization cell supports 12 chart types, and each one serves a specific analytical purpose. This is not a charting library demo. These are the visualizations that quants and professional traders actually use.
-
Area and Line Charts: Equity curves, rolling metrics, time series analysis. The backbone of any trading performance review.
-
Bar and Stacked Bar Charts: Performance comparison across categories. Which assets perform best? Which setups? Which time periods?
-
Scatter Plots: Correlation visualization. Plot signal strength against trade outcome to see the relationship visually.
-
Candlestick Charts: Standard OHLCV charts with overlay indicators for technical analysis.
-
Heatmaps: Time-of-day performance, correlation matrices, regime-performance grids. Dense information display for pattern recognition.
-
Pie and Donut Charts: Portfolio allocation, win/loss distribution, category breakdowns.
-
Waterfall Charts: PnL attribution showing how individual trades contribute to total performance.
Every visualization can be pinned to a Dashboard for persistent monitoring, and all charts are rendered from live query data so they update when you re-run the underlying query.
Live Query cells stream real-time market data directly into your notebook. No API keys. No external connections. No code. Select the data type and the stream begins.
Available live feeds include:
These feeds are particularly powerful when combined with Parameter cells for filtering. Set a threshold for minimum transaction size, select specific exchanges, and you have a custom on-chain intelligence dashboard running in real-time.
For traders who need full programmatic control, the Python cell executes arbitrary Python code in a secure E2B sandbox. This is available on Pro and Founder plans.
You can import standard data science libraries (pandas, numpy, scipy, scikit-learn), write custom analysis logic, and produce outputs that appear directly in the notebook. The sandbox ensures your code runs safely without affecting the system or other users.
Build custom indicators that go beyond what the built-in tools offer. Run machine learning models on your trading data. Implement proprietary statistical tests. Generate publication-quality plots with matplotlib or plotly. Automate complex data transformations that SQL cannot express cleanly.
import pandas as pd
import numpy as np
trades = pd.
DataFrame(query_results) # from a linked SQL cell
trades['date'] = pd.to_datetime(trades['entry_date'])
trades = trades.sort_values('date')
trades['return'] = trades['pnl_percent'] / 100
rolling_sharpe = (
trades['return'].rolling(30).mean() /
trades['return'].rolling(30).std()
) * np.sqrt(252)
print(f"Current 30-trade rolling Sharpe: {rolling_sharpe.iloc[-1]:.2f}")
print(f"Peak Sharpe: {rolling_sharpe.max():.2f}")
print(f"Trough Sharpe: {rolling_sharpe.min():.2f}")
The output appears directly in the notebook, and you can link a Visualization cell to render the rolling Sharpe as a line chart. That gives you a real-time view of how your edge evolves over time—the single most important metric for any systematic trader.
Once you have built analysis in the notebook, you can pin cells to a Dashboard for persistent monitoring. Dashboards support seven widget types: charts, tables, counters, filter controls, markdown text, live feeds, and trading charts.
Widgets are fully configurable and support drag-and-drop layout for both desktop and mobile views. Cross-filtering lets a selection in one widget update the data in another. You can duplicate widgets, refresh data, and configure display settings per widget.
Dashboards and notebooks can be shared publicly with slug-based URLs. Other Thrive users can fork your notebooks to build on your analysis. The community aspect means you benefit from other traders' research and frameworks, not just your own.
Public sharing is particularly valuable for educators, fund managers sharing strategies with investors, and traders who want to build a public track record of their analysis.
The Import cell supports CSV uploads for trade data and custom datasets. You can also sync trades directly from connected exchanges, pulling your complete trading history into the Workbench for analysis. This eliminates the manual data entry that makes most trade journaling systems a chore.
Every notebook exports to Jupyter Notebook (.ipynb) format with one click. The export preserves all cell types, converting SQL queries to code cells, visualizations to image outputs, and markdown to text cells. This means your analysis is portable and can continue in any Jupyter-compatible environment.
Query results also export to CSV for use in spreadsheets or other tools.
The Data Catalog lets you browse every table and column available for querying. Each table includes a description, column list with types, and one-click insertion into SQL cells. You never have to guess at table names or column structures. The catalog is your map to the entire data universe available in the Workbench.
Every operation in the Workbench consumes credits. This pay-for-what-you-use model means you are not paying a flat fee for features you do not use.
| Feature |
Credit Cost |
| AI Chat - Instant |
15 credits |
| AI Chat - Deep Research |
200 credits |
| SQL Query |
Included |
| Correlation Analysis |
5 credits |
| Granger Causality |
10 credits |
| Cointegration Test |
10 credits |
| Regime Detection |
5 credits |
| Multi-Factor Regression |
10 credits |
| Signal Decay Analysis |
10 credits |
| Anomaly Detection |
5 credits |
| Feature Discovery |
15 credits |
| Signal Strength Scoring |
10 credits |
| Ensemble Builder |
15 credits |
| Alpha Leak Detection |
5 credits |
| Report Generation |
25 credits |
| Backtest (Simple) |
30 credits |
| Backtest (Complex) |
50+ credits |
| Trade Analysis |
10 credits |
SQL queries are included with your plan. The credit-consuming features are the computationally intensive tools that require significant processing power. The pricing page shows current credit allocations per subscription tier.
→ View Plans and Credit Allocations
You have SQL access to your personal trading data (trades, balances, positions, watchlists, alerts), market data (signals, events, divergences, sentiment, liquidation events, smart money moves, funding rate history, candlestick data), and workbench data (imported trades, strategies, backtests, custom signals, datasets). All queries are automatically scoped to your account for security.
No. The AI Chat mode lets you ask questions in plain English, and the system generates and executes the appropriate analysis. SQL knowledge unlocks more precise and custom queries, but the AI Chat handles everything from simple data lookups to complex multi-step quantitative analysis without writing a single line of code.
After running a standard backtest, the Monte Carlo simulator runs 500+ iterations by randomizing trade order and applying statistical variation. This produces a probability distribution of outcomes rather than a single result, showing you the range of expected returns at different confidence levels. If your strategy is profitable even in the 5th percentile scenario, the edge is likely robust.
Yes. Python execution is available on Pro and Founder plans. Code runs in a secure E2B sandbox with access to standard data science libraries including pandas, numpy, scipy, and scikit-learn. Python cells can reference data from SQL cells and produce outputs that render directly in the notebook.
The Workbench is included with all Thrive subscription plans. SQL queries are unlimited. AI Chat and quantitative tools consume credits, with costs ranging from 5 credits for basic analysis to 200 credits for deep research. Credit allocations vary by subscription tier. Visit the pricing page for current details.
Yes. Notebooks and dashboards can be shared publicly with slug-based URLs. Other Thrive users can fork your notebooks to build on your research. Dashboards can be embedded and shared with custom slugs for persistent access.
The Strategy Builder is a no-code interface where you define trading rules using dropdown menus and condition builders. It covers the most common strategy types without any programming. Python execution gives you unrestricted programmatic control for custom logic, advanced machine learning models, or analyses beyond what the built-in tools offer.
Yes. Live Query cells stream real-time funding rates, liquidation events, sentiment data, smart money movements, exchange reserves, and whale transactions. These feeds update continuously without manual refresh.
Notebooks export to Jupyter Notebook (.ipynb) format, preserving all cell types and outputs. Query results export to CSV. This makes your analysis fully portable to any Jupyter-compatible environment.
SQL queries have a 10-second timeout and return a maximum of 1,000 rows. This keeps the interface responsive. For larger datasets, use aggregations and filters in your queries. Rate limiting is set at 30 queries per minute.
The Thrive Data Workbench is the most comprehensive quantitative analysis environment available to crypto traders today. It replaces the patchwork of tools that most traders cobble together: a charting platform here, a spreadsheet there, an on-chain analytics subscription for data, and a Python notebook for backtesting. All of that lives in one tab now.
Whether you are a discretionary trader who wants AI-powered insights in plain English, a systematic trader building and validating signal-based strategies, or a quant who needs SQL and Python access to raw data, the Workbench has the tools for your workflow. The 14 cell types cover every stage of the analysis pipeline, from data acquisition through signal generation, backtesting, and performance attribution.
The combination of no-code accessibility and professional-grade depth is what sets this apart. You can start with the AI Chat asking simple questions about your trading performance, and gradually build toward sophisticated multi-factor analysis with Monte Carlo validated backtests. There is no ceiling, and the on-ramp is gentle.
If you are serious about finding and proving your edge in crypto markets, the Data Workbench is where that work happens. Stop guessing. Start proving.
→ Get Started with the Data Workbench
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