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import logging
from dataclasses import dataclass, field
from typing import Optional
from tqdm import tqdm
from graph import NavigationGraph as Graph
from pathfinder import Ship
from arbitrage import Trade
from typing import Any
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
MAX_TRADES_TO_CONSIDER = 20_000
MAX_CAPITAL = 100_000_000
Route = list[dict[str, Any]]
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 = ship.get('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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