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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.

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Why Reports?

ChannelBest For
TelegramQuick notifications, mobile alerts, simple text
ReportsInteractive charts, detailed analysis, scheduled outputs
When you schedule a routine to run every hour, you may not want 24 Telegram messages per day. Instead, generate reports and check them in the dashboard when convenient.

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Viewing Reports

Reports appear in the Condor web dashboard under Routines → Reports. Each report includes:
  • Timestamp
  • Source routine name
  • Interactive HTML content (zoomable charts, expandable sections)
Condor maintains a buffer of the last 30 reports. Older reports are automatically cleaned up.

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Creating Reports

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1. Import ReportBuilder

from routines.report_builder import ReportBuilder

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2. Generate Your Data

Create figures and tables as you normally would: 
import plotly.graph_objects as go

# Create a Plotly figure
fig = go.Figure()
fig.add_trace(go.Scatter(x=dates, y=prices, name="Price"))
fig.update_layout(title="Market Analysis")

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3. Build the Report

At the end of your routine, use ReportBuilder: 
report = ReportBuilder(source="my_routine")
report.add_markdown("## Analysis Summary\n\nKey findings from today's scan.")
report.add_figure(fig)
report.add_table(df)  # Pandas DataFrame
report.save()

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Complete Example

# routines/top_movers.py
from pydantic import BaseModel, Field
from telegram.ext import ContextTypes
import plotly.graph_objects as go

from routines.report_builder import ReportBuilder
from config_manager import get_client


class Config(BaseModel):
    """Analyze top movers and generate a report."""

    exchange: str = Field(default="binance_perpetual", description="Exchange to scan")
    top_n: int = Field(default=10, description="Number of top movers")


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"

    # Fetch top movers data
    movers = await client.market_data.get_top_movers(
        connector_name=config.exchange,
        limit=config.top_n
    )

    gainers = movers["gainers"]
    losers = movers["losers"]

    # Create visualization
    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=[g["symbol"] for g in gainers],
        y=[g["change_pct"] for g in gainers],
        name="Gainers",
        marker_color="green"
    ))
    fig.add_trace(go.Bar(
        x=[l["symbol"] for l in losers],
        y=[l["change_pct"] for l in losers],
        name="Losers",
        marker_color="red"
    ))
    fig.update_layout(title="Top Movers", barmode="group")

    # Generate report
    report = ReportBuilder(source="top_movers")
    report.add_markdown(f"## Top Movers on {config.exchange}\n")
    report.add_markdown(f"**Top Gainer:** {gainers[0]['symbol']} (+{gainers[0]['change_pct']:.2f}%)\n")
    report.add_markdown(f"**Top Loser:** {losers[0]['symbol']} ({losers[0]['change_pct']:.2f}%)\n")
    report.add_figure(fig)
    report.save()

    # Return text for Telegram
    return f"Top mover: {gainers[0]['symbol']} +{gainers[0]['change_pct']:.2f}%"

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ReportBuilder Methods

MethodDescription
add_markdown(text)Add markdown-formatted text
add_figure(fig)Add a Plotly figure (interactive in HTML)
add_table(df)Add a Pandas DataFrame as an HTML table
save()Write the report to disk

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Use Cases

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Scheduled Market Analysis

Run a routine every hour to analyze market conditions: 
/routines → technical_analysis → Schedule → Every 1 hour
Instead of 24 Telegram messages, check the reports dashboard for the latest analysis with interactive charts.

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Agent Decision Logging

When an agent runs a routine for analysis, the report captures what the agent “saw” when making decisions: 
# In agent's analysis routine
report = ReportBuilder(source="bb_trader_analysis")
report.add_markdown("## Agent Analysis\n")
report.add_markdown(f"Current price: ${price}\n")
report.add_markdown(f"Signal: {signal}\n")
report.add_figure(indicator_chart)
report.save()
This helps you understand and debug agent behavior by reviewing the data it analyzed.

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Comparing Tokens

Build research routines that compare multiple assets: 
# Compare DEX tokens: market cap, fees, price
report = ReportBuilder(source="dex_comparison")
report.add_markdown("## Solana DEX Token Comparison")
report.add_figure(market_cap_chart)
report.add_figure(fee_revenue_chart)
report.add_table(comparison_df)
report.save()

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Report Storage

Reports are stored in ~/condor/reports/ as HTML files. The web dashboard reads from this directory. 
ls ~/condor/reports/
# top_movers_2024-01-15_14-30-00.html
# technical_analysis_2024-01-15_15-00-00.html
To clean up reports manually: 
rm ~/condor/reports/*.html
Or use the dashboard’s cleanup function under Routines → Reports → Clean.