def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
try:
df_comb = combine_dataframes_with_mean(data, "close")
except ValueError:
raise OperationalException(
"No data found. Please make sure that data is available for "
"the timerange and pairs selected.")
# Trim trades to available OHLCV data
trades = extract_trades_of_period(df_comb, trades, date_index=True)
if len(trades) == 0:
raise OperationalException('No trades found in selected timerange.')
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
# Plot the pairs average close prices, and total profit growth
avgclose = go.Scatter(
x=df_comb.index,
y=df_comb['mean'],
name='Avg close price',
)
fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
row_heights=[1, 1, 1, 0.5, 1],
vertical_spacing=0.05,
subplot_titles=[
"AVG Close Price",
"Combined Profit",
"Profit per pair",
"Parallelism",
"Underwater",
])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis4'].update(title='Trade count')
fig['layout']['yaxis5'].update(title='Underwater Plot')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
fig = add_parallelism(fig, 4, trades, timeframe)
fig = add_underwater(fig, 5, trades)
for pair in pairs:
profit_col = f'cum_profit_{pair}'
try:
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col,
timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
except ValueError:
pass
return fig
def test_combine_dataframes_with_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
df = combine_dataframes_with_mean(data)
assert isinstance(df, DataFrame)
assert "ETH/BTC" in df.columns
assert "ADA/BTC" in df.columns
assert "mean" in df.columns
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_dataframes_with_mean(data, "close")
# Trim trades to available OHLCV data
trades = extract_trades_of_period(df_comb, trades, date_index=True)
if len(trades) == 0:
raise OperationalException('No trades found in selected timerange.')
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
# Plot the pairs average close prices, and total profit growth
avgclose = go.Scatter(
x=df_comb.index,
y=df_comb['mean'],
name='Avg close price',
)
fig = make_subplots(rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 1],
vertical_spacing=0.05,
subplot_titles=[
"AVG Close Price", "Combined Profit",
"Profit per pair"
])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Profit')
fig['layout']['yaxis3'].update(title='Profit')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
for pair in pairs:
profit_col = f'cum_profit_{pair}'
try:
df_comb = create_cum_profit(df_comb,
trades[trades['pair'] == pair],
profit_col, timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
except ValueError:
pass
return fig
def test_combine_dataframes_with_mean_no_data(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='6m')
with pytest.raises(ValueError, match=r"No objects to concatenate"):
combine_dataframes_with_mean(data)
