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"""
Simulation commands for the Folio CLI.
This module provides commands for simulating portfolio performance under different scenarios.
"""
import copy
from typing import Any
from src.focli.formatters import display_simulation_results
from src.focli.utils import filter_portfolio_groups, parse_args
from src.folio.simulator import (
generate_spy_changes,
simulate_portfolio_with_spy_changes,
)
def simulate_command(args: list[str], state: dict[str, Any], console):
"""Simulate portfolio performance with SPY changes.
Args:
args: Command arguments
state: Application state
console: Rich console for output
"""
# Check if a portfolio is loaded
if not state.get("portfolio_groups"):
console.print("[bold red]Error:[/bold red] No portfolio loaded.")
console.print("Use 'portfolio load <path>' to load a portfolio.")
return
# Check if we have a subcommand or arguments
if not args:
# Default to SPY simulation with default parameters
simulate_spy([], state, console)
return
# Check if the first argument is a subcommand
first_arg = args[0].lower()
if first_arg in ["spy", "scenario"]:
# It's a subcommand
subcommand = first_arg
subcommand_args = args[1:]
if subcommand == "spy":
simulate_spy(subcommand_args, state, console)
elif subcommand == "scenario":
console.print(
"[bold yellow]Note:[/bold yellow] Scenario simulation is not yet implemented."
)
else:
# No subcommand specified, assume SPY simulation with the provided arguments
simulate_spy(args, state, console)
def simulate_spy(args: list[str], state: dict[str, Any], console):
"""Simulate portfolio performance with SPY changes.
Args:
args: Command arguments
state: Application state
console: Rich console for output
"""
# Define argument specifications
arg_specs = {
"range": {
"type": float,
"default": 20.0,
"help": "SPY change range in percent",
"aliases": ["-r", "--range"],
},
"steps": {
"type": int,
"default": 13,
"help": "Number of steps in the simulation",
"aliases": ["-s", "--steps"],
},
"focus": {
"type": str,
"default": None,
"help": "Comma-separated list of tickers to focus on",
"aliases": ["-f", "--focus"],
},
"detailed": {
"type": bool,
"default": False,
"help": "Show detailed analysis for all positions",
"aliases": ["-d", "--detailed"],
},
"preset": {
"type": str,
"default": None,
"help": "Use a parameter preset (default, detailed, quick)",
"aliases": ["-p", "--preset"],
},
"save_preset": {
"type": str,
"default": None,
"help": "Save current parameters as a preset",
"aliases": ["--save-preset"],
},
"filter": {
"type": str,
"default": None,
"help": "Filter positions by type (options, stocks)",
"aliases": ["--filter"],
},
"min_value": {
"type": float,
"default": None,
"help": "Minimum position value to include",
"aliases": ["--min-value"],
},
"max_value": {
"type": float,
"default": None,
"help": "Maximum position value to include",
"aliases": ["--max-value"],
},
}
try:
# Parse arguments
parsed_args = parse_args(args, arg_specs)
# Check if we're using a preset
if parsed_args["preset"]:
preset_name = parsed_args["preset"].lower()
if preset_name in state["simulation_presets"]:
# Load preset parameters
preset = state["simulation_presets"][preset_name]
console.print(f"[bold]Using preset:[/bold] {preset_name}")
# Apply preset parameters (only if not explicitly specified)
for key, value in preset.items():
if key not in parsed_args or parsed_args[key] is None:
parsed_args[key] = value
else:
console.print(f"[bold red]Unknown preset:[/bold red] {preset_name}")
console.print(
f"Available presets: {', '.join(state['simulation_presets'].keys())}"
)
return
# Get parameters
range_pct = parsed_args["range"]
steps = parsed_args["steps"]
focus = parsed_args["focus"]
detailed = parsed_args["detailed"]
# Save preset if requested
if parsed_args["save_preset"]:
preset_name = parsed_args["save_preset"].lower()
preset = {"range": range_pct, "steps": steps, "detailed": detailed}
if focus:
preset["focus"] = focus
state["simulation_presets"][preset_name] = preset
console.print(f"[bold green]Saved preset:[/bold green] {preset_name}")
# Parse focus tickers if provided
focus_tickers = None
if focus:
focus_tickers = [ticker.strip().upper() for ticker in focus.split(",")]
# Apply filtering if requested
portfolio_groups = state["portfolio_groups"]
filter_criteria = {}
if parsed_args["filter"]:
filter_type = parsed_args["filter"].lower()
if filter_type == "options":
filter_criteria["has_options"] = True
elif filter_type == "stocks":
filter_criteria["has_stock"] = True
if parsed_args["min_value"] is not None:
filter_criteria["min_value"] = parsed_args["min_value"]
if parsed_args["max_value"] is not None:
filter_criteria["max_value"] = parsed_args["max_value"]
if focus_tickers:
filter_criteria["tickers"] = focus_tickers
# Apply filters if any criteria are set
if filter_criteria:
filtered_groups = filter_portfolio_groups(portfolio_groups, filter_criteria)
# Print filter summary
filter_desc = []
if filter_criteria.get("tickers"):
filter_desc.append(f"tickers: {', '.join(filter_criteria['tickers'])}")
if filter_criteria.get("has_options") is not None:
filter_desc.append(f"has options: {filter_criteria['has_options']}")
if filter_criteria.get("has_stock") is not None:
filter_desc.append(f"has stock: {filter_criteria['has_stock']}")
if filter_criteria.get("min_value") is not None:
filter_desc.append(f"min value: ${filter_criteria['min_value']:,.2f}")
if filter_criteria.get("max_value") is not None:
filter_desc.append(f"max value: ${filter_criteria['max_value']:,.2f}")
console.print(f"[italic]Filtered by: {'; '.join(filter_desc)}[/italic]")
console.print(
f"[italic]Using {len(filtered_groups)} of {len(portfolio_groups)} positions[/italic]"
)
# Use filtered groups for simulation
portfolio_groups = filtered_groups
# Store filtered groups in state
state["filtered_groups"] = filtered_groups
# Generate SPY changes
spy_changes = generate_spy_changes(range_pct, steps)
# Run the simulation
console.print(
f"[bold]Running simulation with range ±{range_pct}% and {steps} steps...[/bold]"
)
results = simulate_portfolio_with_spy_changes(
portfolio_groups=portfolio_groups,
spy_changes=spy_changes,
cash_like_positions=state["portfolio_summary"].cash_like_positions,
pending_activity_value=state["portfolio_summary"].pending_activity_value,
)
# Store results for future reference
state["last_simulation"] = results
# Add to simulation history (keep last 5)
simulation_copy = copy.deepcopy(results)
simulation_copy["parameters"] = {
"range": range_pct,
"steps": steps,
"detailed": detailed,
"focus": focus,
"timestamp": "now", # In a real implementation, use actual timestamp
}
state["simulation_history"].append(simulation_copy)
if len(state["simulation_history"]) > 5:
state["simulation_history"].pop(0)
# Display the results
display_simulation_results(results, detailed, focus_tickers, console)
except ValueError as e:
console.print(f"[bold red]Error:[/bold red] {e!s}")
except Exception as e:
console.print(f"[bold red]Error running simulation:[/bold red] {e!s}")
import traceback
console.print(traceback.format_exc())
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