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import logging
from dataclasses import dataclass, field
from typing import Optional
from tqdm import tqdm
from graph import Graph
from pathfinder import Ship
from arbitrage import Trade
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
MAX_TRADES_TO_CONSIDER = 20_000
MAX_CAPITAL = 100_000_000
@dataclass
class RouteStep:
location_id: int
station_name: str
actions: list[dict] = field(default_factory=list)
Route = list[RouteStep]
def route(trades: list[Trade], graph: Graph, ship: Ship) -> tuple[Optional[Route], Optional[dict]]:
"""
Given a list of trades, find the best route starting from the ship's location.
Args:
trades: List of Trade instances.
graph: Navigation graph.
ship: Ship parameters.
Returns:
A tuple containing the best route and route information.
"""
trades.sort(
key=lambda trade: trade.gross_profit / (trade.from_price * trade.quantity) if trade.from_price * trade.quantity > 0 else 0,
reverse=True
)
best_profit_rate = 0.0
best_route: Optional[Route] = None
best_route_info: Optional[dict] = None
seen_routes = set()
for trade in tqdm(trades[:MAX_TRADES_TO_CONSIDER], desc="Evaluating trades"):
route_stations = (ship.location, trade.from_station, trade.to_station)
if route_stations in seen_routes:
continue
seen_routes.add(route_stations)
optimized_trades = select_trades(list(route_stations), trades, ship)
total_profit = sum(t.gross_profit for t in optimized_trades)
if not optimized_trades or total_profit <= 0:
continue
try:
path = graph.shortest_path(ship.location, trade.from_station)[:-1]
path += graph.shortest_path(trade.from_station, trade.to_station)
except Exception as e:
logger.error(f"Error finding path: {e}")
continue
risk = sum(graph.graph[u][v]['risk'] for u, v in zip(path, path[1:]))
transport_time = sum(graph.graph[u][v]['time'] for u, v in zip(path, path[1:]))
capital = getattr(ship, 'ship_cost', 0.0) + sum(t.from_price * t.quantity for t in optimized_trades)
if transport_time == 0:
logger.warning("Transport time is zero, skipping this route to avoid division by zero.")
continue
net_profit = total_profit - risk * capital
profit_rate = net_profit / transport_time
if profit_rate > best_profit_rate:
logger.info(
f"New best profit rate: {profit_rate * 3600:.2f} per hour, "
f"Risk: {risk:.4f}, Capital: {capital:.2f}, "
f"Transport time: {transport_time:.2f} sec, Gross profit: {total_profit:.2f}"
)
formatted_route = graph.formatted_route(path)
final_route = set_actions(formatted_route, optimized_trades)
best_route = final_route
best_route_info = {
"profit_rate": profit_rate,
"risk": risk,
"capital": capital,
"transport_time": transport_time,
"gross_profit": total_profit,
"net_profit": net_profit,
}
best_profit_rate = profit_rate
return best_route, best_route_info
def select_trades(stations: list[int], trades: list[Trade], ship: Ship) -> list[Trade]:
"""
Given a list of stations, find the optimal trades to add.
Args:
stations: List of station IDs.
trades: List of Trade instances.
ship: Ship parameters.
Returns:
A list of optimized Trade instances.
"""
filtered_trades = [
trade for trade in trades
if trade.from_station in stations and trade.to_station in stations and
stations.index(trade.from_station) < stations.index(trade.to_station)
]
filtered_trades.sort(
key=lambda trade: trade.gross_profit / trade.cargo if trade.cargo > 0 else 0,
reverse=True
)
used_orders = {}
optimized_trades = []
cargo = 0.0
capital = 0.0
for trade in filtered_trades:
quantity = trade.quantity
from_order, to_order = trade.from_order_id, trade.to_order_id
from_remaining = used_orders.get(from_order, quantity)
to_remaining = used_orders.get(to_order, quantity)
max_quantity_by_cargo = int((ship.cargo - cargo) // trade.item_cargo_volume)
max_quantity_by_capital = int((MAX_CAPITAL - capital) // trade.from_price)
max_quantity = min(quantity, from_remaining, to_remaining, max_quantity_by_cargo, max_quantity_by_capital)
if max_quantity <= 0:
continue
cargo += trade.item_cargo_volume * max_quantity
capital += trade.from_price * max_quantity
used_orders[from_order] = from_remaining - max_quantity
used_orders[to_order] = to_remaining - max_quantity
# Create a new Trade instance with updated quantities
updated_trade = Trade(
from_station=trade.from_station,
to_station=trade.to_station,
from_order_id=trade.from_order_id,
to_order_id=trade.to_order_id,
item_name=trade.item_name,
type_id=trade.type_id,
item_cargo_volume=trade.item_cargo_volume,
from_price=trade.from_price,
to_price=trade.to_price,
quantity=max_quantity,
cargo=trade.item_cargo_volume * max_quantity,
gross_profit=(trade.to_price - trade.from_price) * max_quantity,
net_profit=None # Will be calculated later if needed
)
optimized_trades.append(updated_trade)
if cargo >= ship.cargo or capital >= MAX_CAPITAL:
break
return optimized_trades
def set_actions(route: Route, trades: list[Trade]) -> Route:
"""
Sets the actions (buy/sell) at each step in the route based on the trades.
Args:
route: The route steps.
trades: List of Trade instances.
Returns:
The route with actions set.
"""
bought_items = set()
for trade in trades:
for step in route:
if trade.from_station == step.location_id:
step.actions.append({
"action_type": "buy",
"item": trade.item_name,
"type_id": trade.type_id,
"quantity": trade.quantity,
"price": trade.from_price,
})
bought_items.add(trade.type_id)
if trade.to_station == step.location_id and trade.type_id in bought_items:
step.actions.append({
"action_type": "sell",
"item": trade.item_name,
"type_id": trade.type_id,
"quantity": trade.quantity,
"price": trade.to_price,
})
for step in route:
step.actions.sort(key=lambda action: (action["action_type"], action["item"], action["price"]))
return route
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