"""Display trading positions as Pandas notebook items."""
import datetime
from typing import Iterable
import pandas as pd
from tradeexecutor.ethereum.revert import clean_revert_reason_message
from tradeexecutor.state.position import TradingPosition
from tradeexecutor.state.reserve import ReservePosition
def _ftime(v: datetime.datetime) -> str:
"""Format times"""
if not v:
return ""
return v.strftime('%Y-%m-%d %H:%M')
[docs]def display_positions(positions: Iterable[TradingPosition]) -> pd.DataFrame:
"""Format trading positions for Jupyter Notebook table output.
Display in one table
- All positions
- Their underlying trades
:return:
DataFrame containing positions and trades, values as string formatted
"""
items = []
idx = []
for p in positions:
idx.append(p.position_id)
flags = []
if p.is_frozen():
flags.append("F")
if p.is_repaired():
flags.append("R")
if p.has_unexecuted_trades():
flags.append("UE")
if p.is_stop_loss():
flags.append("SL")
items.append({
"Flags": ", ".join(flags),
"Ticker": p.pair.get_ticker(),
f"Size {p.pair.base.token_symbol}": p.get_quantity(),
"Profit": p.get_realised_profit_percent() * 100 if p.is_closed() else "",
"Opened at": _ftime(p.opened_at),
"Closed at": _ftime(p.closed_at),
"Notes": p.notes,
})
for t in p.trades.values():
idx.append(p.position_id)
text = []
if t.notes:
text.append(t.notes)
revert_reason = clean_revert_reason_message(t.get_revert_reason())
if revert_reason:
text.append(revert_reason)
items.append({
"Trade id": str(t.trade_id), # Mixed NA/number column fix
"Price": t.executed_price,
f"Trade size": t.get_position_quantity(),
"Trade opened": _ftime(t.opened_at),
"Trade executed": _ftime(t.executed_at),
"Trade notes": "\n".join(text),
})
df = pd.DataFrame(items, index=idx)
df = df.fillna("")
df = df.replace({pd.NaT: ""})
return df
[docs]def display_reserve_position_events(position: ReservePosition) -> pd.DataFrame:
"""Display events that cause the balance of the reserve position.
"""
items = []
idx = []
for event in position.get_balance_update_events():
idx.append(event.balance_update_id)
items.append({
"Cause": event.cause.name,
"At": event.block_mined_at,
"Quantity": event.quantity,
"Dollar value": event.usd_value,
"Address": event.owner_address,
"Notes": event.notes,
})
df = pd.DataFrame(items, index=idx)
df = df.fillna("")
df = df.replace({pd.NaT: ""})
return df