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Rename profitperc to profit_percent

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This commit is contained in:
Matthias 2020-06-07 15:17:35 +02:00
parent 04779411f5
commit 3f9ab0846d
4 changed files with 18 additions and 17 deletions
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15
freqtrade/data/btanalysis.py
View File

@ -16,7 +16,7 @@ from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_time", "close_time", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
@ -99,7 +99,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profitperc",
columns = ["pair", "open_time", "close_time", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
@ -190,7 +190,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
@ -201,7 +201,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
@ -211,13 +212,13 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
value_col: str = 'profitperc'
value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""

14
freqtrade/plot/plotting.py
View File

@ -162,7 +162,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profitperc'] * 100, 1)}%, "
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min",
axis=1)
trade_buys = go.Scatter(
@ -181,9 +181,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
trade_sells = go.Scatter(
x=trades.loc[trades['profitperc'] > 0, "close_time"],
y=trades.loc[trades['profitperc'] > 0, "close_rate"],
text=trades.loc[trades['profitperc'] > 0, "desc"],
x=trades.loc[trades['profit_percent'] > 0, "close_time"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
marker=dict(
@ -194,9 +194,9 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profitperc'] <= 0, "close_time"],
y=trades.loc[trades['profitperc'] <= 0, "close_rate"],
text=trades.loc[trades['profitperc'] <= 0, "desc"],
x=trades.loc[trades['profit_percent'] <= 0, "close_time"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"],
mode='markers',
name='Sell - Loss',
marker=dict(

2
tests/data/test_btanalysis.py
View File

@ -47,7 +47,7 @@ def test_load_trades_from_db(default_conf, fee, mocker):
assert isinstance(trades, DataFrame)
assert "pair" in trades.columns
assert "open_time" in trades.columns
assert "profitperc" in trades.columns
assert "profit_percent" in trades.columns
for col in BT_DATA_COLUMNS:
if col not in ['index', 'open_at_end']:

4
tests/test_plotting.py
View File

@ -124,7 +124,7 @@ def test_plot_trades(testdatadir, caplog):
trade_sell = find_trace_in_fig_data(figure.data, 'Sell - Profit')
assert isinstance(trade_sell, go.Scatter)
assert trade_sell.yaxis == 'y'
assert len(trades.loc[trades['profitperc'] > 0]) == len(trade_sell.x)
assert len(trades.loc[trades['profit_percent'] > 0]) == len(trade_sell.x)
assert trade_sell.marker.color == 'green'
assert trade_sell.marker.symbol == 'square-open'
assert trade_sell.text[0] == '4.0%, roi, 15 min'
@ -132,7 +132,7 @@ def test_plot_trades(testdatadir, caplog):
trade_sell_loss = find_trace_in_fig_data(figure.data, 'Sell - Loss')
assert isinstance(trade_sell_loss, go.Scatter)
assert trade_sell_loss.yaxis == 'y'
assert len(trades.loc[trades['profitperc'] <= 0]) == len(trade_sell_loss.x)
assert len(trades.loc[trades['profit_percent'] <= 0]) == len(trade_sell_loss.x)
assert trade_sell_loss.marker.color == 'red'
assert trade_sell_loss.marker.symbol == 'square-open'
assert trade_sell_loss.text[5] == '-10.4%, stop_loss, 720 min'