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1932 lines (1564 loc) · 70.5 KB
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"""Main CLI interface for QuantCoder - inspired by Mistral Vibe CLI."""
import click
import logging
import sys
from pathlib import Path
from rich.console import Console
from rich.logging import RichHandler
from rich.panel import Panel
from rich.markdown import Markdown
from .config import Config
from .chat import InteractiveChat
from .tools import (
SearchArticlesTool,
DownloadArticleTool,
SummarizeArticleTool,
GenerateCodeTool,
ValidateCodeTool,
BacktestTool,
)
console = Console()
def setup_logging(verbose: bool = False, config: Config = None):
"""Configure logging with rich handler and rotation.
Uses the new centralized logging system with:
- Log rotation (configurable size and backup count)
- Structured JSON logging (in addition to console output)
- Optional webhook alerting for errors
"""
from quantcoder.logging_config import setup_logging as setup_qc_logging
# Get logging config if available
logging_config = None
if config:
try:
logging_config = config.get_logging_config()
except Exception:
pass # Use defaults if config fails
# Create rich console handler
rich_handler = RichHandler(
rich_tracebacks=True,
console=console,
show_time=True,
show_path=False,
)
rich_handler._custom_formatter = True # Signal to not override formatter
# Setup centralized logging
setup_qc_logging(
verbose=verbose,
config=logging_config,
console_handler=rich_handler,
)
@click.group(invoke_without_command=True)
@click.option('--verbose', '-v', is_flag=True, help='Enable verbose logging')
@click.option('--config', type=click.Path(), help='Path to config file')
@click.option('--prompt', '-p', type=str, help='Run in non-interactive mode with prompt')
@click.pass_context
def main(ctx, verbose, config, prompt):
"""
QuantCoder - AI-powered CLI for generating QuantConnect algorithms.
A conversational interface to transform research articles into trading algorithms.
"""
# Load configuration first so logging can use it
config_path = Path(config) if config else None
cfg = Config.load(config_path)
# Setup logging with config (enables rotation, JSON logs, webhooks)
setup_logging(verbose, cfg)
ctx.ensure_object(dict)
ctx.obj['config'] = cfg
ctx.obj['verbose'] = verbose
# If prompt is provided, run in non-interactive mode
if prompt:
from .chat import ProgrammaticChat
chat = ProgrammaticChat(cfg)
result = chat.process(prompt)
console.print(result)
return
# If no subcommand, launch interactive mode
if ctx.invoked_subcommand is None:
interactive(cfg)
def interactive(config: Config):
"""Launch interactive chat mode."""
banner = (
"[bold cyan]"
" ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗████████╗\n"
" ██╔═══██╗██║ ██║██╔══██╗████╗ ██║╚══██╔══╝\n"
" ██║ ██║██║ ██║███████║██╔██╗ ██║ ██║\n"
" ██║▄▄ ██║██║ ██║██╔══██║██║╚██╗██║ ██║\n"
" ╚██████╔╝╚██████╔╝██║ ██║██║ ╚████║ ██║\n"
" ╚══▀▀═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝\n"
" ██████╗ ██████╗ ██████╗ ███████╗██████╗\n"
" ██╔════╝██╔═══██╗██╔══██╗██╔════╝██╔══██╗\n"
" ██║ ██║ ██║██║ ██║█████╗ ██████╔╝\n"
" ██║ ██║ ██║██║ ██║██╔══╝ ██╔══██╗\n"
" ╚██████╗╚██████╔╝██████╔╝███████╗██║ ██║\n"
" ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝[/bold cyan]\n"
"\n"
" [dim]v2.0 | Local AI-powered QuantConnect algorithm pipeline[/dim]\n"
" [dim]Type 'help' for commands, 'exit' to quit[/dim]"
)
console.print(Panel(banner, border_style="cyan", padding=(1, 2)))
chat = InteractiveChat(config)
chat.run()
@main.command()
@click.argument('query')
@click.option('--num', default=5, help='Number of results to return')
@click.option('--deep', is_flag=True, help='Use Tavily for semantic deep search (requires TAVILY_API_KEY)')
@click.option('--no-filter', is_flag=True, help='Skip LLM relevance filtering (with --deep)')
@click.pass_context
def search(ctx, query, num, deep, no_filter):
"""
Search for academic articles.
By default uses CrossRef API for keyword search.
With --deep flag, uses Tavily for semantic search + LLM filtering.
Examples:
quantcoder search "algorithmic trading" --num 3
quantcoder search "momentum strategy" --deep
quantcoder search "mean reversion" --deep --num 10
"""
config = ctx.obj['config']
if deep:
# Use Tavily deep search
from .tools import DeepSearchTool
tool = DeepSearchTool(config)
with console.status(f"Deep searching for '{query}'..."):
result = tool.execute(
query=query,
max_results=num,
filter_relevance=not no_filter,
)
if result.success:
console.print(f"[green]✓[/green] {result.message}\n")
for idx, article in enumerate(result.data, 1):
score = article.get('relevance_score', 0)
score_color = "green" if score > 0.7 else "yellow" if score > 0.5 else "dim"
published = f" ({article['published']})" if article.get('published') else ""
console.print(
f" [cyan]{idx}.[/cyan] {article['title']}\n"
f" [{score_color}]Score: {score:.2f}[/{score_color}]{published}\n"
f" [dim]{article['URL'][:60]}...[/dim]"
)
console.print(f"\n[dim]Use 'quantcoder download <ID>' to get articles[/dim]")
else:
console.print(f"[red]✗[/red] {result.error}")
else:
# Use arXiv search (default, open-access)
tool = SearchArticlesTool(config)
with console.status(f"Searching arXiv for '{query}'..."):
result = tool.execute(query=query, max_results=num)
if result.success:
console.print(f"[green]✓[/green] {result.message}")
for idx, article in enumerate(result.data, 1):
published = f" ({article['published']})" if article.get('published') else ""
cats = article.get('categories', [])
cat_str = f" [magenta][{', '.join(cats[:3])}][/magenta]" if cats else ""
console.print(
f" [cyan]{idx}.[/cyan] {article['title']}\n"
f" [dim]{article['authors']}{published}[/dim]{cat_str}"
)
else:
console.print(f"[red]✗[/red] {result.error}")
@main.command()
@click.argument('article_ids', type=int, nargs=-1, required=True)
@click.pass_context
def download(ctx, article_ids):
"""
Download article PDF(s) by ID.
Examples:
quantcoder download 1
quantcoder download 1 2 3
"""
config = ctx.obj['config']
tool = DownloadArticleTool(config)
for article_id in article_ids:
with console.status(f"Downloading article {article_id}..."):
result = tool.execute(article_id=article_id)
if result.success:
console.print(f"[green]✓[/green] Article {article_id}: {result.message}")
else:
console.print(f"[red]✗[/red] Article {article_id} download failed:")
for line in result.error.split("\n"):
console.print(f" [yellow]{line}[/yellow]")
@main.command()
@click.argument('article_ids', type=int, nargs=-1, required=True)
@click.pass_context
def summarize(ctx, article_ids):
"""
Summarize downloaded article(s).
When multiple articles are provided, also creates a consolidated summary
with a new ID that can be used with 'generate'.
Examples:
quantcoder summarize 1
quantcoder summarize 1 2 3 # Creates individual + consolidated summary
"""
config = ctx.obj['config']
tool = SummarizeArticleTool(config)
article_ids_list = list(article_ids)
with console.status(f"Analyzing article(s) {article_ids_list}..."):
result = tool.execute(article_ids=article_ids_list)
if result.success:
console.print(f"[green]✓[/green] {result.message}\n")
# Show individual summaries
for summary in result.data.get('summaries', []):
console.print(Panel(
Markdown(summary.get('summary_text', '')),
title=f"Summary #{summary.get('article_id')} - {summary.get('title', 'Unknown')[:50]}",
border_style="green"
))
# Highlight consolidated summary if created
if result.data.get('consolidated_summary_id'):
consolidated_id = result.data['consolidated_summary_id']
console.print(Panel(
f"[bold]Consolidated summary created: #{consolidated_id}[/bold]\n\n"
f"Source articles: {article_ids_list}\n\n"
f"Use [cyan]quantcoder generate {consolidated_id}[/cyan] to generate code from the combined strategy.",
title="Consolidated Summary",
border_style="cyan"
))
else:
console.print(f"[red]✗[/red] {result.error}")
@main.command(name='summaries')
@click.pass_context
def list_summaries(ctx):
"""
List all available summaries (individual and consolidated).
Shows summary IDs that can be used with 'generate' command.
"""
from quantcoder.core.summary_store import SummaryStore
config = ctx.obj['config']
store = SummaryStore(config.home_dir)
summaries = store.list_summaries()
if not summaries['individual'] and not summaries['consolidated']:
console.print("[yellow]No summaries found. Use 'summarize' to create some.[/yellow]")
return
from rich.table import Table
# Individual summaries
if summaries['individual']:
table = Table(title="Individual Summaries")
table.add_column("ID", style="cyan")
table.add_column("Article", style="white")
table.add_column("Title", style="green")
table.add_column("Type", style="yellow")
for s in summaries['individual']:
table.add_row(
str(s['summary_id']),
str(s['article_id']),
s['title'][:50] + "..." if len(s['title']) > 50 else s['title'],
s['strategy_type']
)
console.print(table)
console.print()
# Consolidated summaries
if summaries['consolidated']:
table = Table(title="Consolidated Summaries")
table.add_column("ID", style="cyan")
table.add_column("Source Articles", style="white")
table.add_column("Type", style="yellow")
table.add_column("Created", style="dim")
for s in summaries['consolidated']:
table.add_row(
str(s['summary_id']),
str(s['source_article_ids']),
s['strategy_type'],
s.get('created_at', '')[:10] if s.get('created_at') else ''
)
console.print(table)
console.print("\n[dim]Use 'quantcoder generate <ID>' to generate code from any summary[/dim]")
def _publish_to_notion(config, summary_id: int, code: str, sharpe: float,
backtest_data: dict, console):
"""Publish strategy article to Notion after successful backtest."""
import os
from quantcoder.core.summary_store import SummaryStore
# Check Notion credentials
notion_key = os.getenv('NOTION_API_KEY')
notion_db = os.getenv('NOTION_DATABASE_ID')
if not notion_key or not notion_db:
console.print("[yellow]⚠ Notion credentials not configured[/yellow]")
console.print(f"[dim]Set NOTION_API_KEY and NOTION_DATABASE_ID in {config.home_dir / '.env'}[/dim]")
console.print("[dim]Use 'quantcoder schedule config' to configure[/dim]")
return
try:
from quantcoder.scheduler import NotionClient, StrategyArticle
# Get summary data
store = SummaryStore(config.home_dir)
summary = store.get_summary(summary_id)
if not summary:
console.print(f"[yellow]⚠ Could not retrieve summary {summary_id} for article[/yellow]")
return
# Determine title and description based on summary type
if summary.get('is_consolidated'):
paper_title = f"Consolidated from articles {summary.get('source_article_ids', [])}"
description = summary.get('merged_description', '')
strategy_type = summary.get('merged_strategy_type', 'hybrid')
paper_url = ""
authors = []
else:
paper_title = summary.get('title', f'Strategy {summary_id}')
description = summary.get('summary_text', '')
strategy_type = summary.get('strategy_type', 'unknown')
paper_url = summary.get('url', '')
authors = [summary.get('authors', 'Unknown')]
# Generate article title based on performance
if sharpe >= 1.5:
perf_label = "High-Performance"
elif sharpe >= 1.0:
perf_label = "Strong"
elif sharpe >= 0.5:
perf_label = "Viable"
else:
perf_label = "Experimental"
strategy_type_display = strategy_type.replace("_", " ").title()
title = f"{perf_label} {strategy_type_display} Strategy"
# Build backtest results for article
backtest_results = {
'sharpe_ratio': sharpe,
'total_return': backtest_data.get('total_return', 0),
'max_drawdown': backtest_data.get('statistics', {}).get('Max Drawdown', 0),
'win_rate': backtest_data.get('statistics', {}).get('Win Rate', 'N/A'),
}
# Create StrategyArticle directly
article = StrategyArticle(
title=title,
paper_title=paper_title,
paper_url=paper_url,
paper_authors=authors,
strategy_summary=description,
strategy_type=strategy_type,
backtest_results=backtest_results,
code_snippet=code[:2000] if len(code) > 2000 else code,
tags=[strategy_type_display]
)
# Publish to Notion
notion_client = NotionClient(api_key=notion_key, database_id=notion_db)
page = notion_client.create_strategy_page(article)
if page:
console.print(f"[green]✓ Published to Notion[/green] (page: {page.id[:8]}...)")
else:
console.print("[yellow]⚠ Failed to create Notion page[/yellow]")
except ImportError as e:
console.print(f"[yellow]⚠ Scheduler module not available: {e}[/yellow]")
except Exception as e:
console.print(f"[red]✗ Failed to publish to Notion: {e}[/red]")
def _run_evolution(config, code: str, source_name: str, max_generations: int,
variants_per_gen: int, start_date: str, end_date: str, console):
"""Run evolution on a strategy to improve it."""
import asyncio
import os
try:
from quantcoder.evolver import EvolutionEngine, EvolutionConfig
# Get QC credentials
qc_user = os.getenv('QC_USER_ID') or os.getenv('QUANTCONNECT_USER_ID')
qc_token = os.getenv('QC_API_TOKEN') or os.getenv('QUANTCONNECT_API_KEY')
qc_project = os.getenv('QC_PROJECT_ID')
if not all([qc_user, qc_token]):
console.print("[yellow]⚠ QC credentials not fully configured for evolution[/yellow]")
return None
# Create evolution config
evo_config = EvolutionConfig(
qc_user_id=qc_user,
qc_api_token=qc_token,
qc_project_id=int(qc_project) if qc_project else None,
max_generations=max_generations,
variants_per_generation=variants_per_gen,
backtest_start_date=start_date,
backtest_end_date=end_date,
)
engine = EvolutionEngine(evo_config)
# Progress callback
def on_gen_complete(state, gen):
best = state.elite_pool.get_best()
if best and best.fitness:
console.print(f" [dim]Gen {gen}: Best fitness = {best.fitness:.4f}[/dim]")
engine.on_generation_complete = on_gen_complete
async def run_evo():
return await engine.evolve(code, source_name)
# Run evolution
with console.status("Evolving strategy..."):
result = asyncio.run(run_evo())
# Get best variant
best = engine.get_best_variant()
if best and best.code:
return {
'code': best.code,
'sharpe': best.metrics.get('sharpe_ratio', 0) if best.metrics else 0,
'backtest_data': best.metrics or {},
'evolution_id': result.evolution_id,
}
return None
except ImportError as e:
console.print(f"[yellow]⚠ Evolution module not available: {e}[/yellow]")
return None
except Exception as e:
console.print(f"[red]✗ Evolution failed: {e}[/red]")
return None
@main.command(name='generate')
@click.argument('summary_id', type=int)
@click.option('--max-attempts', default=6, help='Maximum refinement attempts')
@click.option('--open-in-editor', is_flag=True, help='Open generated code in editor (default: Zed)')
@click.option('--editor', default=None, help='Editor to use (overrides config, e.g., zed, code, vim)')
@click.option('--backtest', is_flag=True, help='Run backtest on QuantConnect after generation')
@click.option('--min-sharpe', default=0.5, type=float, help='Min Sharpe to keep algo and publish to Notion (with --backtest)')
@click.option('--start-date', default='2020-01-01', help='Backtest start date (with --backtest)')
@click.option('--end-date', default='2024-01-01', help='Backtest end date (with --backtest)')
@click.option('--evolve', is_flag=True, help='Evolve strategy after backtest passes (with --backtest)')
@click.option('--gens', default=5, type=int, help='Number of evolution generations (with --evolve)')
@click.option('--variants', default=3, type=int, help='Variants per generation (with --evolve)')
@click.pass_context
def generate_code(ctx, summary_id, max_attempts, open_in_editor, editor, backtest, min_sharpe, start_date, end_date, evolve, gens, variants):
"""
Generate QuantConnect code from a summary.
SUMMARY_ID can be:
- An individual article summary ID
- A consolidated summary ID (created from multiple articles)
With --backtest flag:
- Runs backtest on QuantConnect after code generation
- If Sharpe >= min-sharpe: keeps algo in QC and publishes article to Notion
- If Sharpe < min-sharpe: reports results but does not publish
With --evolve flag (requires --backtest):
- After backtest passes, evolves the strategy for N generations
- Publishes the best evolved variant to Notion
Examples:
quantcoder generate 1 # From article 1 summary
quantcoder generate 6 # From consolidated summary #6
quantcoder generate 1 --open-in-editor
quantcoder generate 1 --backtest # Generate, backtest, and publish if good
quantcoder generate 1 --backtest --min-sharpe 1.0
quantcoder generate 1 --backtest --evolve --gens 5 # Evolve after backtest
"""
config = ctx.obj['config']
tool = GenerateCodeTool(config)
with console.status(f"Generating code for summary #{summary_id}..."):
result = tool.execute(summary_id=summary_id, max_refine_attempts=max_attempts)
if result.success:
console.print(f"[green]✓[/green] {result.message}\n")
# Display summary
if result.data.get('summary'):
console.print(Panel(
Markdown(result.data['summary']),
title="Strategy Summary",
border_style="blue"
))
# Display code
from rich.syntax import Syntax
code_display = Syntax(
result.data['code'],
"python",
theme="monokai",
line_numbers=True
)
console.print("\n")
console.print(Panel(
code_display,
title="Generated Code",
border_style="green"
))
# Open in editor if requested
if open_in_editor:
from .editor import open_in_editor as launch_editor, get_editor_display_name
editor_cmd = editor or config.ui.editor
editor_name = get_editor_display_name(editor_cmd)
code_path = result.data.get('path')
if code_path:
if launch_editor(code_path, editor_cmd):
console.print(f"[cyan]Opened in {editor_name}[/cyan]")
else:
console.print(f"[yellow]Could not open in {editor_name}. Is it installed?[/yellow]")
# Handle backtest if requested
if backtest:
code_path = result.data.get('path')
if not code_path:
console.print("[red]✗[/red] Cannot backtest: no code file path")
return
# Check QuantConnect credentials
if not config.has_quantconnect_credentials():
console.print("[red]Error: QuantConnect credentials not configured[/red]")
console.print(f"[yellow]Please set QUANTCONNECT_API_KEY and QUANTCONNECT_USER_ID in {config.home_dir / '.env'}[/yellow]")
return
console.print("\n")
backtest_tool = BacktestTool(config)
with console.status(f"Running backtest ({start_date} to {end_date})..."):
bt_result = backtest_tool.execute(
file_path=code_path,
start_date=start_date,
end_date=end_date,
name=f"Summary_{summary_id}"
)
if not bt_result.success:
console.print(f"[red]✗[/red] Backtest failed: {bt_result.error}")
return
try:
sharpe = float(bt_result.data.get('sharpe_ratio', 0))
except (TypeError, ValueError):
sharpe = 0.0
console.print(f"[green]✓[/green] Backtest complete: Sharpe = {sharpe:.2f}")
# Display backtest results
from rich.table import Table
bt_table = Table(title="Backtest Results")
bt_table.add_column("Metric", style="cyan")
bt_table.add_column("Value", style="green")
bt_table.add_row("Sharpe Ratio", f"{sharpe:.2f}")
bt_table.add_row("Total Return", str(bt_result.data.get('total_return', 'N/A')))
cagr = bt_result.data.get('cagr')
bt_table.add_row("CAGR", f"{cagr:.1%}" if isinstance(cagr, (int, float)) else "N/A")
max_dd = bt_result.data.get('max_drawdown')
bt_table.add_row("Max Drawdown", f"{max_dd:.1%}" if isinstance(max_dd, (int, float)) else "N/A")
win_rate = bt_result.data.get('win_rate')
bt_table.add_row("Win Rate", f"{win_rate:.1%}" if isinstance(win_rate, (int, float)) else "N/A")
total_trades = bt_result.data.get('total_trades')
bt_table.add_row("Total Trades", str(total_trades) if total_trades is not None else "N/A")
console.print(bt_table)
# Check acceptance criteria
if sharpe >= min_sharpe:
console.print(f"\n[green]✓ Sharpe {sharpe:.2f} >= {min_sharpe} - ACCEPTED[/green]")
final_code = result.data['code']
final_sharpe = sharpe
final_backtest_data = bt_result.data
# Run evolution if requested
if evolve:
console.print(f"\n[cyan]Evolving strategy for {gens} generations...[/cyan]")
evolved_result = _run_evolution(
config=config,
code=result.data['code'],
source_name=f"Summary_{summary_id}",
max_generations=gens,
variants_per_gen=variants,
start_date=start_date,
end_date=end_date,
console=console
)
if evolved_result:
final_code = evolved_result['code']
final_sharpe = evolved_result['sharpe']
final_backtest_data = evolved_result['backtest_data']
console.print(f"[green]✓ Evolution complete: Sharpe improved to {final_sharpe:.2f}[/green]")
# Publish to Notion
console.print("[cyan]Publishing to Notion...[/cyan]")
_publish_to_notion(
config=config,
summary_id=summary_id,
code=final_code,
sharpe=final_sharpe,
backtest_data=final_backtest_data,
console=console
)
else:
console.print(f"\n[yellow]⚠ Sharpe {sharpe:.2f} < {min_sharpe} - NOT PUBLISHED[/yellow]")
console.print("[dim]Strategy kept locally but not published to Notion[/dim]")
else:
console.print(f"[red]✗[/red] {result.error}")
@main.command(name='validate')
@click.argument('file_path', type=click.Path(exists=True))
@click.option('--local-only', is_flag=True, help='Only run local syntax check, skip QuantConnect')
@click.pass_context
def validate_code_cmd(ctx, file_path, local_only):
"""
Validate algorithm code locally and on QuantConnect.
Example:
quantcoder validate generated_code/algorithm_1.py
quantcoder validate my_algo.py --local-only
"""
config = ctx.obj['config']
tool = ValidateCodeTool(config)
# Read the file
with open(file_path, 'r') as f:
code = f.read()
with console.status(f"Validating {file_path}..."):
result = tool.execute(code=code, use_quantconnect=not local_only)
if result.success:
console.print(f"[green]✓[/green] {result.message}")
if result.data and result.data.get('warnings'):
console.print("[yellow]Warnings:[/yellow]")
for w in result.data['warnings']:
console.print(f" • {w}")
else:
console.print(f"[red]✗[/red] {result.error}")
if result.data and result.data.get('errors'):
console.print("[red]Errors:[/red]")
for err in result.data['errors'][:10]:
console.print(f" • {err}")
@main.command(name='backtest')
@click.argument('file_path', type=click.Path(exists=True))
@click.option('--start', default='2020-01-01', help='Backtest start date (YYYY-MM-DD)')
@click.option('--end', default='2024-01-01', help='Backtest end date (YYYY-MM-DD)')
@click.option('--name', help='Name for the backtest')
@click.pass_context
def backtest_cmd(ctx, file_path, start, end, name):
"""
Run backtest on QuantConnect.
Requires QUANTCONNECT_API_KEY and QUANTCONNECT_USER_ID in ~/.quantcoder/.env
Example:
quantcoder backtest generated_code/algorithm_1.py
quantcoder backtest my_algo.py --start 2022-01-01 --end 2024-01-01
"""
config = ctx.obj['config']
# Check credentials first
if not config.has_quantconnect_credentials():
console.print("[red]Error: QuantConnect credentials not configured[/red]")
console.print(f"[yellow]Please set QUANTCONNECT_API_KEY and QUANTCONNECT_USER_ID in {config.home_dir / '.env'}[/yellow]")
return
tool = BacktestTool(config)
with console.status(f"Running backtest on {file_path} ({start} to {end})..."):
result = tool.execute(file_path=file_path, start_date=start, end_date=end, name=name)
if result.success:
console.print(f"[green]✓[/green] {result.message}\n")
# Display results table
from rich.table import Table
table = Table(title="Backtest Results")
table.add_column("Metric", style="cyan")
table.add_column("Value", style="green")
table.add_row("Backtest ID", str(result.data.get('backtest_id', 'N/A')))
sharpe = result.data.get('sharpe_ratio')
try:
table.add_row("Sharpe Ratio", f"{float(sharpe):.2f}" if sharpe is not None else "N/A")
except (ValueError, TypeError):
table.add_row("Sharpe Ratio", str(sharpe))
table.add_row("Total Return", str(result.data.get('total_return') or 'N/A'))
cagr = result.data.get('cagr')
table.add_row("CAGR", f"{cagr:.1%}" if isinstance(cagr, (int, float)) else "N/A")
max_dd = result.data.get('max_drawdown')
table.add_row("Max Drawdown", f"{max_dd:.1%}" if isinstance(max_dd, (int, float)) else "N/A")
win_rate = result.data.get('win_rate')
table.add_row("Win Rate", f"{win_rate:.1%}" if isinstance(win_rate, (int, float)) else "N/A")
total_trades = result.data.get('total_trades')
table.add_row("Total Trades", str(total_trades) if total_trades is not None else "N/A")
console.print(table)
else:
console.print(f"[red]✗[/red] {result.error}")
@main.command()
@click.pass_context
def config_show(ctx):
"""Show current configuration."""
config = ctx.obj['config']
config_text = f"""
**Model Configuration:**
- Provider: {config.model.provider}
- Model: {config.model.model}
- Temperature: {config.model.temperature}
- Max Tokens: {config.model.max_tokens}
**UI Configuration:**
- Theme: {config.ui.theme}
- Auto Approve: {config.ui.auto_approve}
- Show Token Usage: {config.ui.show_token_usage}
**Tools Configuration:**
- Downloads Directory: {config.tools.downloads_dir}
- Generated Code Directory: {config.tools.generated_code_dir}
- Enabled Tools: {', '.join(config.tools.enabled_tools)}
**Paths:**
- Home Directory: {config.home_dir}
- Config File: {config.home_dir / 'config.toml'}
"""
console.print(Panel(
Markdown(config_text),
title="Configuration",
border_style="cyan"
))
@main.command()
def version():
"""Show version information."""
from . import __version__
console.print(f"QuantCoder v{__version__}")
# ============================================================================
# AUTONOMOUS MODE COMMANDS
# ============================================================================
@main.group()
def auto():
"""
Autonomous self-improving mode for strategy generation.
This mode runs continuously, learning from errors and improving over time.
"""
pass
@auto.command(name='start')
@click.option('--query', required=True, help='Strategy query (e.g., "momentum trading")')
@click.option('--max-iterations', default=50, help='Maximum iterations to run')
@click.option('--min-sharpe', default=0.5, type=float, help='Minimum Sharpe ratio threshold')
@click.option('--output', type=click.Path(), help='Output directory for strategies')
@click.option('--demo', is_flag=True, help='Run in demo mode (no real API calls)')
@click.pass_context
def auto_start(ctx, query, max_iterations, min_sharpe, output, demo):
"""
Start autonomous strategy generation.
Example:
quantcoder auto start --query "momentum trading" --max-iterations 50
"""
import asyncio
from pathlib import Path
from quantcoder.autonomous import AutonomousPipeline
config = ctx.obj['config']
if demo:
console.print("[yellow]Running in DEMO mode (no real API calls)[/yellow]\n")
output_dir = Path(output) if output else None
pipeline = AutonomousPipeline(
config=config,
demo_mode=demo
)
try:
asyncio.run(pipeline.run(
query=query,
max_iterations=max_iterations,
min_sharpe=min_sharpe,
output_dir=output_dir
))
except KeyboardInterrupt:
console.print("\n[yellow]Autonomous mode stopped by user[/yellow]")
@auto.command(name='status')
def auto_status():
"""
Show autonomous mode status and learning statistics.
"""
from quantcoder.autonomous.database import LearningDatabase
db = LearningDatabase()
# Show library stats
stats = db.get_library_stats()
console.print("\n[bold cyan]Autonomous Mode Statistics[/bold cyan]\n")
console.print(f"Total strategies generated: {stats.get('total_strategies', 0)}")
console.print(f"Successful: {stats.get('successful', 0)}")
console.print(f"Average Sharpe: {stats.get('avg_sharpe', 0):.2f}\n")
# Show common errors
console.print("[bold cyan]Common Errors:[/bold cyan]")
from quantcoder.autonomous.learner import ErrorLearner
learner = ErrorLearner(db)
errors = learner.get_common_errors(limit=5)
for i, error in enumerate(errors, 1):
fix_rate = (error['fixed_count'] / error['count'] * 100) if error['count'] > 0 else 0
console.print(f" {i}. {error['error_type']}: {error['count']} ({fix_rate:.0f}% fixed)")
db.close()
@auto.command(name='report')
@click.option('--format', type=click.Choice(['text', 'json']), default='text')
def auto_report(format):
"""
Generate learning report from autonomous mode.
"""
from quantcoder.autonomous.database import LearningDatabase
db = LearningDatabase()
stats = db.get_library_stats()
if format == 'json':
import json
console.print(json.dumps(stats, indent=2))
else:
# Text format
console.print("\n[bold cyan]Autonomous Mode Learning Report[/bold cyan]\n")
console.print("=" * 60)
# Overall stats
console.print(f"\nTotal Strategies: {stats.get('total_strategies', 0)}")
console.print(f"Successful: {stats.get('successful', 0)}")
console.print(f"Average Sharpe: {stats.get('avg_sharpe', 0):.2f}")
console.print(f"Average Errors: {stats.get('avg_errors', 0):.1f}")
console.print(f"Average Refinements: {stats.get('avg_refinements', 0):.1f}")
# Category breakdown
if stats.get('categories'):
console.print("\n[bold]Category Breakdown:[/bold]")
for cat in stats['categories']:
console.print(f" • {cat['category']}: {cat['count']} strategies (avg Sharpe: {cat['avg_sharpe']:.2f})")
db.close()
# ============================================================================
# LIBRARY BUILDER MODE COMMANDS
# ============================================================================
@main.group()
def library():
"""
Library builder mode - Build comprehensive strategy library from scratch.
This mode systematically generates strategies across all major categories.
"""
pass
@library.command(name='build')
@click.option('--comprehensive', is_flag=True, help='Build all categories')
@click.option('--max-hours', default=24, type=int, help='Maximum build time in hours')
@click.option('--output', type=click.Path(), help='Output directory for library')
@click.option('--min-sharpe', default=0.5, type=float, help='Minimum Sharpe ratio threshold')
@click.option('--categories', help='Comma-separated list of categories to build')
@click.option('--demo', is_flag=True, help='Run in demo mode (no real API calls)')
@click.pass_context
def library_build(ctx, comprehensive, max_hours, output, min_sharpe, categories, demo):
"""
Build strategy library from scratch.
Example:
quantcoder library build --comprehensive --max-hours 24
quantcoder library build --categories momentum,mean_reversion
"""
import asyncio
from pathlib import Path
from quantcoder.library import LibraryBuilder
config = ctx.obj['config']
if demo:
console.print("[yellow]Running in DEMO mode (no real API calls)[/yellow]\n")
output_dir = Path(output) if output else None
category_list = categories.split(',') if categories else None
builder = LibraryBuilder(
config=config,
demo_mode=demo
)
try:
asyncio.run(builder.build(
comprehensive=comprehensive,
max_hours=max_hours,
output_dir=output_dir,
min_sharpe=min_sharpe,
categories=category_list
))
except KeyboardInterrupt:
console.print("\n[yellow]Library build stopped by user[/yellow]")
@library.command(name='status')
def library_status():
"""
Show library build progress.
"""
import asyncio
from quantcoder.library import LibraryBuilder
builder = LibraryBuilder()
try:
asyncio.run(builder.status())
except FileNotFoundError:
console.print("[yellow]No library build in progress[/yellow]")