async def exchange_balances_extra_df(
self, # type: HummingbotApplication
ex_balances: Dict[str, Decimal],
ex_avai_balances: Dict[str, Decimal]):
total_col_name = f"Total ({RateOracle.global_token_symbol})"
allocated_total = Decimal("0")
rows = []
for token, bal in ex_balances.items():
if bal == Decimal(0):
continue
avai = Decimal(ex_avai_balances.get(
token.upper(),
0)) if ex_avai_balances is not None else Decimal(0)
allocated = f"{(bal - avai) / bal:.0%}"
rate = await RateOracle.global_rate(token)
rate = Decimal("0") if rate is None else rate
global_value = rate * bal
allocated_total += rate * (bal - avai)
rows.append({
"Asset":
token.upper(),
"Total":
round(bal, 4),
total_col_name:
PerformanceMetrics.smart_round(global_value),
"Allocated":
allocated,
})
df = pd.DataFrame(
data=rows, columns=["Asset", "Total", total_col_name, "Allocated"])
df.sort_values(by=["Asset"], inplace=True)
return df, allocated_total
async def market_trades_report(
self, # type: HummingbotApplication
connector,
days: float,
market: str):
trades: List[Trade] = await connector.get_my_trades(market, days)
g_sym = RateOracle.global_token_symbol
if not trades:
self._notify(f"There is no trade on {market}.")
return
data = []
amount_g_col_name = f" Amount ({g_sym})"
columns = ["Time", " Side", " Price", "Amount", amount_g_col_name]
trades = sorted(trades, key=lambda x: (x.trading_pair, x.timestamp))
fees = {} # a dict of token and total fee amount
fee_usd = 0
for trade in trades:
time = f"{datetime.fromtimestamp(trade.timestamp / 1e3).strftime('%Y-%m-%d %H:%M:%S')} "
side = "buy" if trade.side is TradeType.BUY else "sell"
usd = await RateOracle.global_value(
trade.trading_pair.split("-")[0], trade.amount)
data.append([
time, side,
PerformanceMetrics.smart_round(trade.price),
PerformanceMetrics.smart_round(trade.amount),
round(usd)
])
for fee in trade.trade_fee.flat_fees:
if fee[0] not in fees:
fees[fee[0]] = fee[1]
else:
fees[fee[0]] += fee[1]
fee_usd += await RateOracle.global_value(fee[0], fee[1])
lines = []
df: pd.DataFrame = pd.DataFrame(data=data, columns=columns)
lines.extend([f" {market.upper()}"])
lines.extend(
[" " + line for line in df.to_string(index=False).split("\n")])
self._notify("\n" + "\n".join(lines))
fee_text = ",".join(k + ": " + f"{v:.4f}" for k, v in fees.items())
self._notify(
f"\n Total traded: {g_sym} {df[amount_g_col_name].sum():.0f} "
f"Fees: {fee_text} ({g_sym} {fee_usd:.2f})")
def profitability_df(self):
data = []
columns = [
"Id", f"{self.base_asset} Change", f"{self.quote_asset} Change",
f"Unclaimed {self.base_asset}", f"Unclaimed {self.quote_asset}",
"Total Profit/loss (%)"
]
for position in self.active_positions:
profit_data = self.calculate_profitability(position)
data.append([
position.token_id,
PerformanceMetrics.smart_round(profit_data['base_change'], 8),
PerformanceMetrics.smart_round(profit_data['quote_change'], 8),
PerformanceMetrics.smart_round(profit_data['base_fee'], 8),
PerformanceMetrics.smart_round(profit_data['quote_fee'], 8),
PerformanceMetrics.smart_round(profit_data['profitability'], 8)
])
return pd.DataFrame(data=data, columns=columns)
def quotes_rate_df(self):
columns = ["Quotes pair", "Rate"]
quotes_pair: str = f"{self._market_info_2.quote_asset}-{self._market_info_1.quote_asset}"
data = [[
quotes_pair,
PerformanceMetrics.smart_round(self._rate_source.rate(quotes_pair))
]]
return pd.DataFrame(data=data, columns=columns)
def active_positions_df(self) -> pd.DataFrame:
columns = [
"Id", "Symbol", "Fee Tier", "Range", "Current Base",
"Current Quote", ""
]
data = []
if len(self.active_positions) > 0:
for position in self.active_positions:
data.append([
position.token_id, position.trading_pair,
position.fee_tier,
f"{PerformanceMetrics.smart_round(Decimal(str(position.lower_price)), 8)} - "
f"{PerformanceMetrics.smart_round(Decimal(str(position.upper_price)), 8)}",
PerformanceMetrics.smart_round(
Decimal(str(position.current_base_amount)), 8),
PerformanceMetrics.smart_round(
Decimal(str(position.current_quote_amount)), 8),
"[In range]" if self._last_price >= position.lower_price
and self._last_price <= position.upper_price else
"[Out of range]"
])
return pd.DataFrame(data=data, columns=columns)
def test_smart_round(self):
value = PerformanceMetrics.smart_round(None)
self.assertIsNone(value)
value = PerformanceMetrics.smart_round(Decimal("NaN"))
self.assertTrue(value.is_nan())
value = PerformanceMetrics.smart_round(Decimal("10000.123456789"))
self.assertEqual(value, Decimal("10000"))
value = PerformanceMetrics.smart_round(Decimal("100.123456789"))
self.assertEqual(value, Decimal("100.1"))
value = PerformanceMetrics.smart_round(Decimal("1.123456789"))
self.assertEqual(value, Decimal("1.12"))
value = PerformanceMetrics.smart_round(Decimal("0.123456789"))
self.assertEqual(value, Decimal("0.1234"))
value = PerformanceMetrics.smart_round(Decimal("0.000456789"))
self.assertEqual(value, Decimal("0.00045"))
value = PerformanceMetrics.smart_round(Decimal("0.000056789"))
self.assertEqual(value, Decimal("0.00005678"))
value = PerformanceMetrics.smart_round(Decimal("0"))
self.assertEqual(value, Decimal("0"))
value = PerformanceMetrics.smart_round(Decimal("0.123456"), 2)
self.assertEqual(value, Decimal("0.12"))
def active_positions_df(self) -> pd.DataFrame:
columns = [
"Symbol", "Type", "Fee Tier", "Amount", "Upper Price",
"Lower Price", ""
]
data = []
if len(self.active_positions) > 0:
for position in self.active_positions:
amount = self._base_token_amount if position in self.active_buys else self._quote_token_amount
data.append([
position.trading_pair,
"Buy" if position in self.active_buys else "Sell",
position.fee_tier,
f"{PerformanceMetrics.smart_round(Decimal(str(amount)), 8)}",
PerformanceMetrics.smart_round(
Decimal(str(position.upper_price)), 8),
PerformanceMetrics.smart_round(
Decimal(str(position.lower_price)), 8),
"[In range]" if self._last_price >= position.lower_price
and self._last_price <= position.upper_price else
"[Out of range]"
])
return pd.DataFrame(data=data, columns=columns)
async def exchange_balances_extra_df(
self, # type: HummingbotApplication
exchange: str,
ex_balances: Dict[str, Decimal],
ex_avai_balances: Dict[str, Decimal]):
conn_setting = AllConnectorSettings.get_connector_settings()[exchange]
total_col_name = f"Total ({RateOracle.global_token_symbol})"
allocated_total = Decimal("0")
rows = []
for token, bal in ex_balances.items():
avai = Decimal(ex_avai_balances.get(
token.upper(),
0)) if ex_avai_balances is not None else Decimal(0)
# show zero balances if it is a gateway connector (the user manually
# chose to show those values with 'gateway connector-tokens')
if conn_setting.uses_gateway_generic_connector():
if bal == Decimal(0):
allocated = "0%"
else:
allocated = f"{(bal - avai) / bal:.0%}"
else:
# the exchange is CEX. Only show balance if non-zero.
if bal == Decimal(0):
continue
allocated = f"{(bal - avai) / bal:.0%}"
rate = await RateOracle.global_rate(token)
rate = Decimal("0") if rate is None else rate
global_value = rate * bal
allocated_total += rate * (bal - avai)
rows.append({
"Asset":
token.upper(),
"Total":
round(bal, 4),
total_col_name:
PerformanceMetrics.smart_round(global_value),
"sum_not_for_show":
global_value,
"Allocated":
allocated,
})
df = pd.DataFrame(data=rows,
columns=[
"Asset", "Total", total_col_name,
"sum_not_for_show", "Allocated"
])
df.sort_values(by=["Asset"], inplace=True)
return df, allocated_total
async def format_status(self) -> str:
"""
Returns a status string formatted to display nicely on terminal. The strings composes of 4 parts: market,
assets, spread and warnings(if any).
"""
if not self._connector_ready:
return "UniswapV3 connector not ready."
columns = ["Exchange", "Market", "Current Price"]
data = []
market, trading_pair, base_asset, quote_asset = self._market_info
data.append([
market.display_name, trading_pair,
PerformanceMetrics.smart_round(Decimal(str(self._last_price)), 8)
])
markets_df = pd.DataFrame(data=data, columns=columns)
lines = []
lines.extend(["", " Markets:"] + [
" " + line
for line in markets_df.to_string(index=False).split("\n")
])
# See if there're any active positions.
if len(self.active_positions) > 0:
pos_info_df = self.active_positions_df()
lines.extend(["", " Positions:"] + [
" " + line
for line in pos_info_df.to_string(index=False).split("\n")
])
pos_profitability_df = self.profitability_df()
lines.extend(["", " Positions Performance:"] + [
" " + line for line in pos_profitability_df.to_string(
index=False).split("\n")
])
else:
lines.extend(["", " No active positions."])
assets_df = self.wallet_balance_data_frame([self._market_info])
lines.extend(["", " Assets:"] +
[" " + line for line in str(assets_df).split("\n")])
warning_lines = self.network_warning([self._market_info])
warning_lines.extend(self.balance_warning([self._market_info]))
if len(warning_lines) > 0:
lines.extend(["", "*** WARNINGS ***"] + warning_lines)
return "\n".join(lines)
def report_performance_by_market(
self, # type: HummingbotApplication
market: str,
trading_pair: str,
perf: PerformanceMetrics,
precision: int):
lines = []
base, quote = trading_pair.split("-")
lines.extend([f"\n{market} / {trading_pair}"])
trades_columns = ["", "buy", "sell", "total"]
trades_data = [
[
f"{'Number of trades':<27}", perf.num_buys, perf.num_sells,
perf.num_trades
],
[
f"{f'Total trade volume ({base})':<27}",
PerformanceMetrics.smart_round(perf.b_vol_base, precision),
PerformanceMetrics.smart_round(perf.s_vol_base, precision),
PerformanceMetrics.smart_round(perf.tot_vol_base, precision)
],
[
f"{f'Total trade volume ({quote})':<27}",
PerformanceMetrics.smart_round(perf.b_vol_quote, precision),
PerformanceMetrics.smart_round(perf.s_vol_quote, precision),
PerformanceMetrics.smart_round(perf.tot_vol_quote, precision)
],
[
f"{'Avg price':<27}",
PerformanceMetrics.smart_round(perf.avg_b_price, precision),
PerformanceMetrics.smart_round(perf.avg_s_price, precision),
PerformanceMetrics.smart_round(perf.avg_tot_price, precision)
],
]
trades_df: pd.DataFrame = pd.DataFrame(data=trades_data,
columns=trades_columns)
lines.extend(["", " Trades:"] + [
" " + line
for line in trades_df.to_string(index=False).split("\n")
])
assets_columns = ["", "start", "current", "change"]
assets_data = [
[f"{base:<17}", "-", "-", "-"] if market in DERIVATIVES
else # No base asset for derivatives because they are margined
[
f"{base:<17}",
PerformanceMetrics.smart_round(perf.start_base_bal, precision),
PerformanceMetrics.smart_round(perf.cur_base_bal, precision),
PerformanceMetrics.smart_round(perf.tot_vol_base, precision)
],
[
f"{quote:<17}",
PerformanceMetrics.smart_round(perf.start_quote_bal,
precision),
PerformanceMetrics.smart_round(perf.cur_quote_bal, precision),
PerformanceMetrics.smart_round(perf.tot_vol_quote, precision)
],
[
f"{trading_pair + ' price':<17}",
PerformanceMetrics.smart_round(perf.start_price),
PerformanceMetrics.smart_round(perf.cur_price),
PerformanceMetrics.smart_round(perf.cur_price -
perf.start_price)
],
[f"{'Base asset %':<17}", "-", "-", "-"] if market in DERIVATIVES
else # No base asset for derivatives because they are margined
[
f"{'Base asset %':<17}", f"{perf.start_base_ratio_pct:.2%}",
f"{perf.cur_base_ratio_pct:.2%}",
f"{perf.cur_base_ratio_pct - perf.start_base_ratio_pct:.2%}"
],
]
assets_df: pd.DataFrame = pd.DataFrame(data=assets_data,
columns=assets_columns)
lines.extend(["", " Assets:"] + [
" " + line
for line in assets_df.to_string(index=False).split("\n")
])
perf_data = [
[
"Hold portfolio value ",
f"{PerformanceMetrics.smart_round(perf.hold_value, precision)} {quote}"
],
[
"Current portfolio value ",
f"{PerformanceMetrics.smart_round(perf.cur_value, precision)} {quote}"
],
[
"Trade P&L ",
f"{PerformanceMetrics.smart_round(perf.trade_pnl, precision)} {quote}"
]
]
perf_data.extend([
"Fees paid ",
f"{PerformanceMetrics.smart_round(fee_amount, precision)} {fee_token}"
] for fee_token, fee_amount in perf.fees.items())
perf_data.extend([[
"Total P&L ",
f"{PerformanceMetrics.smart_round(perf.total_pnl, precision)} {quote}"
], ["Return % ", f"{perf.return_pct:.2%}"]])
perf_df: pd.DataFrame = pd.DataFrame(data=perf_data)
lines.extend(["", " Performance:"] + [
" " + line for line in perf_df.to_string(
index=False, header=False).split("\n")
])
self._notify("\n".join(lines))
async def format_status(self) -> str:
"""
Returns a status string formatted to display nicely on terminal. The strings composes of 4 parts: markets,
assets, profitability and warnings(if any).
"""
if self._all_arb_proposals is None:
return " The strategy is not ready, please try again later."
columns = ["Exchange", "Market", "Sell Price", "Buy Price", "Mid Price"]
data = []
for market_info in [self._market_info_1, self._market_info_2]:
market, trading_pair, base_asset, quote_asset = market_info
buy_price = await market.get_quote_price(trading_pair, True, self._order_amount)
sell_price = await market.get_quote_price(trading_pair, False, self._order_amount)
# check for unavailable price data
buy_price = PerformanceMetrics.smart_round(Decimal(str(buy_price)), 8) if buy_price is not None else '-'
sell_price = PerformanceMetrics.smart_round(Decimal(str(sell_price)), 8) if sell_price is not None else '-'
mid_price = PerformanceMetrics.smart_round(((buy_price + sell_price) / 2), 8) if '-' not in [buy_price, sell_price] else '-'
data.append([
market.display_name,
trading_pair,
sell_price,
buy_price,
mid_price
])
markets_df = pd.DataFrame(data=data, columns=columns)
lines = []
lines.extend(["", " Markets:"] + [" " + line for line in markets_df.to_string(index=False).split("\n")])
columns = ["Exchange", "Gas Fees"]
data = []
for market_info in [self._market_info_1, self._market_info_2]:
if hasattr(market_info.market, "network_transaction_fee"):
transaction_fee: TokenAmount = getattr(market_info.market, "network_transaction_fee")
data.append([market_info.market.display_name, f"{transaction_fee.amount} {transaction_fee.token}"])
network_fees_df = pd.DataFrame(data=data, columns=columns)
if len(data) > 0:
lines.extend(
["", " Network Fees:"] +
[" " + line for line in network_fees_df.to_string(index=False).split("\n")]
)
assets_df = self.wallet_balance_data_frame([self._market_info_1, self._market_info_2])
lines.extend(["", " Assets:"] +
[" " + line for line in str(assets_df).split("\n")])
lines.extend(["", " Profitability:"] + self.short_proposal_msg(self._all_arb_proposals))
quotes_rates_df = self.quotes_rate_df()
lines.extend(["", f" Quotes Rates ({str(self._rate_source)})"] +
[" " + line for line in str(quotes_rates_df).split("\n")])
warning_lines = self.network_warning([self._market_info_1])
warning_lines.extend(self.network_warning([self._market_info_2]))
warning_lines.extend(self.balance_warning([self._market_info_1]))
warning_lines.extend(self.balance_warning([self._market_info_2]))
if len(warning_lines) > 0:
lines.extend(["", "*** WARNINGS ***"] + warning_lines)
return "\n".join(lines)