Quant Trading
Data Agent for Trading

Your market data,
analyzed by AI.

Connect exchange APIs. Ask questions in plain language. Get indicators, screens, alerts, and morning briefings — without writing pandas scripts, maintaining cron jobs, or rebuilding your Jupyter notebook every Monday.

Try free with your data See what it does
The problem

You have the data. You lack the hours.

Exchange APIs are free. Python libraries are free. The scarce resource is the time between "I have a hypothesis" and "I have an answer." Every quant researcher knows the ritual: pull data, clean it, compute indicators, visualize, repeat. Most of the work is plumbing, not thinking.

01

Jupyter is powerful, but manual

Every analysis starts from scratch. You re-import the same libraries, re-fetch the same data, re-compute the same indicators. The notebook doesn't remember what you did last week.

02

ChatGPT helps, but forgets

Upload a CSV, get a great analysis, close the tab — and the data is gone. No persistence, no scheduling, no alerts. You can't tell ChatGPT "run this again tomorrow."

03

Data is everywhere, answers are nowhere

Binance for price. DeFiLlama for TVL. CoinGecko for market cap. Twitter for sentiment. Five tabs, five formats, zero cross-source analysis. The question you actually want to ask spans all of them.

How it works

From exchange API to insight in three steps.

1

Connect

Sync exchange data via API connectors, import historical candles from CSV or Parquet, or push data through webhooks. Everything lands in PostgreSQL with TimescaleDB — a time-series database built for exactly this workload.

Binance API CoinGecko CSV / Parquet REST API Webhook
2

Ask

Ask any question about your market data in plain language. The agent writes SQL, runs Python with pandas and ta-lib, generates charts — and explains what it found. No query language to learn. No notebook to maintain.

"What was the Sharpe ratio for BTC over the last 90 days?" "When did ETH and SOL correlation break down?" "Which tokens in my watchlist have RSI below 30 right now?"
3

Keep

Every analysis becomes an asset. Save it as a dashboard that refreshes with each sync, a script that runs every morning, a watch that alerts on signal triggers, or a report your team can reference.

Dashboard Watch / Alert Daily briefing Reusable script
In action

Two conversations. Two real outcomes.

These are the kinds of questions quant researchers actually ask — and the kind of answers that used to require an hour of Jupyter work.

07:15 · Pre-market
Alex
Crypto researcher
independent fund

Alex tracks 80+ tokens. Every morning he checks which ones are showing oversold conditions with rising volume — a setup he's backtested manually before. He opens Tablize to screen his entire universe in one question.

What he keptA daily screening script that runs at 7 AM and drops results into Slack.
crypto-research · signal scan
Wednesday · 07:15
Alex
Screen my watchlist for RSI below 30, 24h volume up more than 50% vs 7d average, and price above the 200-day MA. Rank by volume.
binance_ohlcv · 84 tokens · hourly candles
Tablize
✓ sql.query · latest OHLCV for 84 tokens 218 ms
✓ python.analyze · RSI(14) + 200d MA + volume ratio 1.2 s
3 tokens match all three conditions right now:
TokenRSI(14)Vol ratioPrice vs 200d MA
AVAX24.3+82%+12%
LINK27.1+64%+8%
INJ29.8+51%+3%
AVAX has the strongest volume spike. Its RSI hasn't been this low since March 12, when price bounced 18% in the following 5 days. LINK shows a similar pattern from April 3 (12% bounce over 4 days).
09:00 · Monday review
Mei
Fund analyst
3-person crypto fund

Mei runs the weekly performance review for her fund. She used to spend 40 minutes pulling data from 3 exchanges and building the same spreadsheet. Now the report arrives in Slack before she finishes her coffee.

What she keptA weekly portfolio report that runs every Monday at 8 AM.
fund-ops · weekly review
Monday · 09:00
Mei
Show me last week's portfolio performance. Break down PnL by position, compare to BTC benchmark, and flag any position with drawdown above 15%.
portfolio_positions · 12 tokens · daily snapshots
Tablize
✓ sql.query · position snapshots + price data last 7d 286 ms
✓ python.analyze · PnL attribution + benchmark comparison 1.6 s
Portfolio return last week: +3.8% vs BTC benchmark +1.2%. Alpha: +2.6%. Two positions need attention:
PositionWeightWeek PnLDrawdown
BTC35%+1.2%-4%
ETH25%+5.1%-8%
SOL15%+8.4%-6%
MATIC10%-4.2%-18%
DOGE5%-7.8%-22%
MATIC and DOGE exceed the 15% drawdown threshold. DOGE has been declining for 11 consecutive days — the longest streak in its history in your portfolio. MATIC drawdown accelerated after Friday's broader alt sell-off.
Capabilities

Six things every quant needs.
All through conversation.

No modules to install. No notebooks to maintain. Tell the agent what you need — it sets up the data pipeline, the analysis, and the monitoring.

01
Market data sync

Every exchange, one connection away.

Connect Binance, CoinGecko, or any REST API — the agent syncs K-lines, tickers, order book snapshots, and on-chain metrics on a schedule you set. No cron jobs to maintain. No scripts to babysit. Tell it what data you want and how often, and it lands in your database ready to query.

Try this prompt "Connect Binance and sync BTC, ETH, and SOL hourly candles. Keep the last 180 days up to date."
integrations.connectjob.schedulesql.query
02
Technical indicators

RSI, MACD, Bollinger — computed, not coded.

Ask for any standard indicator and the agent writes the pandas + ta-lib code, runs it in a sandboxed Python environment, and returns results as a table or chart. Save the script and schedule it to run after every data sync — your indicators stay current without manual work.

Try this prompt "Compute 14-period RSI and 20-day Bollinger Bands for BTC. Show me every time price touched the lower band in the last 90 days."
python.analyzescript.saveapp.dashboard
03
Factor screening

Multi-condition filters in one sentence.

Describe your signal in plain language and the agent translates it into SQL + Python. Screen hundreds of tokens at once — by momentum, volatility, volume profile, or any custom metric you define. Results come back as a ranked table you can export or turn into a dashboard.

Try this prompt "Screen all tokens on Binance for RSI below 30, 24h volume above $10M, and price above the 200-day MA. Rank by volume."
sql.querypython.analyzereport.save
04
Correlation & regime analysis

See what moves together — and when it stops.

The agent computes rolling correlations, PCA decomposition, and regime-change detection across your asset universe. Ask "when did BTC-ETH correlation break down?" and get a dated answer with context, not a static chart you have to interpret yourself.

Try this prompt "Compute 30-day rolling correlation between BTC, ETH, SOL, and AVAX for the last year. Flag periods where correlation dropped below 0.5."
python.analyzesql.queryapp.dashboard
05
Price alerts & watches

Any condition. Checked every sync.

Define a watch in one sentence — price threshold, indicator trigger, volume spike, drawdown limit. The agent creates a SQL condition that runs after every data sync. When it fires, you get notified via Slack, email, or webhook. No polling scripts. No third-party alert services.

Try this prompt "Alert me when BTC drops more than 5% in a single day, or when RSI crosses above 70 after being below 30 within the last 7 days."
watch.createnotify.slacksql.query
06
Automated briefing

The morning report that writes itself.

Configure a daily or weekly briefing in one conversation. The agent pulls the latest data, computes your chosen metrics, writes a narrative summary, and delivers it on schedule. Overnight moves, top movers, indicator signals, portfolio drift — all in one report, delivered before you open your charts.

Try this prompt "Every morning at 7 AM, generate a market briefing: BTC and ETH overnight change, top 5 gainers and losers by 24h volume, and any RSI divergences in my watchlist. Send to Slack."
script.savejob.schedulereport.save
How it
compares

Not a trading platform.
The analysis layer you keep rebuilding.

Tablize doesn't execute trades or run backtests. It handles the data work that eats your mornings — so you spend time on alpha, not on plumbing.

Tablize ChatGPT Jupyter TradingView JoinQuant
Setup time Same day Every session 1–2 days Minutes 1–2 hours
AI analysis Built in Built in — — —
Data persists Yes, in database No, session only Files on disk Read only Platform only
Custom data sources Any API or CSV File upload only Manual code Built-in only Built-in only
Scheduled reports One sentence — Manual cron — Limited
Alerts & watches Any SQL condition — — Price only Strategy only
Backtesting — — Custom code Pine Script Full engine
Order execution — — Custom code Some brokers Broker API
Starting price $20/mo $20/mo Free $15/mo Free
Where it fits

The AI analysis layer
between your data
and your decisions.

Tablize doesn't replace your backtest engine, your trading bot, or your broker. It sits upstream — pulling market data, computing features, screening signals, and monitoring conditions — so the tools downstream get cleaner inputs and you get faster answers.

Tablize Data sync + AI analysis + alerts + automated reports
feeds into
Your trading stack Backtest engines, trading bots, broker APIs, Jupyter notebooks
connects to
Exchanges & data sources Binance, OKX, CoinGecko, on-chain APIs, news feeds
Who it's for

If one of these sounds like you.

Crypto researcher Solo or small fund

You track 50+ tokens across 3 exchanges. Every morning you pull data, compute indicators, scan for setups. Tablize automates the entire pipeline — so you spend time on thesis, not on data plumbing.

Quant developer Has own backtest engine

You already have backtesting in Python or vnpy. What you don't have is a clean data layer. Tablize syncs exchange data, computes features, and exposes them via SQL — so your backtest engine reads from a database instead of a pile of CSV files.

Fund data engineer Supporting a team

The PMs want a daily market briefing. The risk team wants drawdown alerts. The researchers want ad-hoc queries. You're building three systems. Tablize is one platform that does all three — and the PMs can self-serve their own questions.

DeFi analyst On-chain + off-chain

You combine CEX price data, DEX volume, TVL, and social sentiment. The data lives in 5 different APIs. Tablize pulls them all into one database and lets you ask cross-source questions in plain language.

Honest limits

What Tablize does not do.

We'd rather be clear now than disappoint you later. Tablize is a data analysis agent. These things are outside its scope:

No

Order execution

Tablize never places, modifies, or cancels orders. It analyzes data and sends you alerts — you make the trading decision.

No

Backtesting engine

No event-driven simulation or slippage modeling. For backtests, use your existing engine (backtrader, vnpy, QuantConnect) — and feed it data from Tablize.

No

Tick-level latency

Data syncs on a schedule (minutes, not milliseconds). This is built for daily and hourly research, not high-frequency trading.

No

Strategy templates

Tablize doesn't ship pre-built strategies. It helps you build, test, and monitor your own — starting from your own hypotheses.

Get started

Your market data already
has the signal. Find it.

Drop a CSV of historical candles, connect an exchange API, or just describe your data. Ask the question you'd normally spend a morning on. See what comes back in two minutes.

Try free with your data See pricing