"""
Your First FX Backtest
======================

This tutorial runs a complete, reproducible FX backtest with TradeTide.  It
uses the bundled EUR/USD data, so no data-provider account or download is
required.  The strategy is deliberately small: Bollinger Bands identify
potential mean-reversion entries, while fixed take-profit and stop-loss levels
make the risk rules explicit.

The result is a compact four-panel report showing price and signals, equity,
open positions, and drawdown.  It is an example workflow—not investment advice
or a claim about live-trading performance.
"""

# %%
# Imports
# -------
#
# The public API keeps the essential pieces together: market data, an
# indicator-backed strategy, exit rules, capital management, and a backtester.

from TradeTide import Backtester, BacktestResult, Currency, Market, Strategy
from TradeTide import capital_management, exit_strategy
from TradeTide import BollingerBands
from TradeTide.times import days, minutes


# %%
# Load bundled market data
# ------------------------
#
# ``time_span`` can be a ``timedelta`` (as used here) or a compact duration
# string such as ``"3d 6h"``.  TradeTide ships samples for EUR/USD, GBP/USD,
# CHF/USD, JPY/USD, and CAD/USD.

market = Market()
market.load_from_database(
    currency_0=Currency.EUR,
    currency_1=Currency.USD,
    time_span=5 * days,
)


# %%
# Define the signal rule
# ----------------------
#
# Bollinger Bands compare price with a rolling average and its volatility band.
# Adding the indicator to a strategy lets the backtester derive trade signals
# from it.

strategy = Strategy()
strategy.add_indicator(BollingerBands(window=30 * minutes, multiplier=2.0))


# %%
# Set risk and sizing rules
# -------------------------
#
# Keep the example's assumptions visible.  ``Static`` exits each position at a
# four-pip stop loss or take profit; ``FixedLot`` caps both individual size and
# simultaneous exposure.

risk_rules = exit_strategy.Static(stop_loss=4, take_profit=4)
position_sizing = capital_management.FixedLot(
    capital=100_000,
    fixed_lot_size=10_000,
    max_capital_at_risk=10_000,
    max_concurrent_positions=1,
)


# %%
# Run and inspect the backtest
# ----------------------------
#
# ``run`` performs signal evaluation, position handling, and portfolio
# simulation.  The chart is a useful first sanity check; evaluate strategies on
# appropriate out-of-sample data before making any trading decision.

backtester = Backtester(
    strategy=strategy,
    market=market,
    exit_strategy=risk_rules,
    capital_management=position_sizing,
)
backtester.run()
result = BacktestResult.from_portfolio(backtester.portfolio)
result.plot_equity_drawdown()