Routines can generate HTML reports with interactive Plotly charts and markdown content. Reports are viewable in the Condor web dashboard, providing a richer experience than Telegram messages for data visualization.
Why Reports?
| Channel | Best For |
|---|
| Telegram | Quick notifications, mobile alerts, simple text |
| Reports | Interactive charts, detailed analysis, scheduled outputs |
Viewing Reports
Reports appear in the Condor web dashboard under Routines → Reports. Open one in the full-screen viewer and use the left/right arrow keys to step through a routine’s report history.
Each report includes:
- Title and creation time
- Source routine name and tags
- Interactive HTML content (zoomable charts, expandable sections)
Condor keeps a rolling buffer of the most recent reports (default 100, set by the CONDOR_MAX_REPORTS environment variable). Older reports are cleaned up automatically.
Creating Reports
1. Import ReportBuilder
from condor.reports import ReportBuilder
2. Generate Your Data
Create figures and tables as you normally would:
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=dates, y=prices, name="Price"))
fig.update_layout(title="Market Analysis")
3. Build the Report
ReportBuilder takes a title, and its methods are chainable. Tag the report with its source, add sections, then await save():
builder = ReportBuilder("Analysis Summary")
builder.source("routine", "my_routine").tags(["analysis"])
builder.markdown("## Analysis Summary\n\nKey findings from today's scan.")
builder.plotly(fig)
builder.table([{"symbol": "SOL", "change": "+4.2%"}])
await builder.save()
Complete Example
# routines/price_snapshot.py
from pydantic import BaseModel, Field
from telegram.ext import ContextTypes
import plotly.graph_objects as go
from config_manager import get_client
from condor.reports import ReportBuilder
class Config(BaseModel):
"""Snapshot prices for a set of pairs and save a report."""
exchange: str = Field(default="binance_perpetual", description="Exchange to query")
pairs: list[str] = Field(
default=["BTC-USDT", "ETH-USDT", "SOL-USDT"],
description="Trading pairs to snapshot",
)
async def run(config: Config, context: ContextTypes.DEFAULT_TYPE) -> str:
chat_id = context._chat_id if hasattr(context, "_chat_id") else None
client = await get_client(chat_id, context=context)
if not client:
return "No server available"
prices = await client.market_data.get_prices(config.exchange, config.pairs)
rows = [{"pair": pair, "price": price} for pair, price in prices.items()]
fig = go.Figure(go.Bar(x=[r["pair"] for r in rows], y=[r["price"] for r in rows]))
fig.update_layout(title=f"Prices on {config.exchange}")
builder = ReportBuilder(f"Price Snapshot — {config.exchange}")
builder.source("routine", "price_snapshot").tags(["prices"])
builder.markdown(f"## Prices on {config.exchange}")
builder.plotly(fig)
builder.table(rows)
await builder.save()
# Return text for Telegram
return f"Saved price snapshot for {len(rows)} pairs"
ReportBuilder Methods
All methods return the builder, so they can be chained.
| Method | Description |
|---|
source(source_type, source_name) | Tag where the report came from (e.g. "routine", "price_snapshot") |
tags(tags) | Attach a list of string tags |
kpi(label, value, delta=None, trend="neutral") | Add a KPI tile |
markdown(text) | Add markdown-formatted text |
plotly(fig) | Add a Plotly figure (interactive in HTML) |
table(rows, columns=None) | Add a table from a list of dicts |
await save(report_id=None) | Write the report to disk; pass an existing id to update in place |
Use Cases
Scheduled Market Analysis
Run a routine every hour to analyze market conditions:
/routines → technical_analysis → Schedule → Every 1 hour
Agent Decision Logging
When an agent runs a routine for analysis, the report captures what the agent “saw” when making decisions:
builder = ReportBuilder("Agent Analysis")
builder.source("routine", "bb_trader_analysis").tags(["agent"])
builder.markdown(f"## Agent Analysis\n\nCurrent price: ${price}\n\nSignal: {signal}")
builder.plotly(indicator_chart)
await builder.save()
Comparing Tokens
Build research routines that compare multiple assets:
builder = ReportBuilder("Solana DEX Token Comparison")
builder.source("routine", "dex_comparison").tags(["solana", "dex"])
builder.markdown("## Solana DEX Token Comparison")
builder.plotly(market_cap_chart)
builder.plotly(fee_revenue_chart)
builder.table(comparison_rows)
await builder.save()
Report Storage
Reports are written as HTML files to the reports/ directory in your Condor install (~/condor/reports/), with an index in reports_index.json. The web dashboard reads from this directory.
ls ~/condor/reports/
# 20260115_143000_price-snapshot_a1b2c3.html
# 20260115_150000_market-scanner_d4e5f6.html
rm ~/condor/reports/*.html
