def test_generate_profit_graph(testdatadir):
filename = testdatadir / "backtest_results/backtest-result_new.json"
trades = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112")
pairs = ["TRX/BTC", "XLM/BTC"]
trades = trades[
trades['close_date'] < pd.Timestamp('2018-01-12', tz='UTC')]
data = history.load_data(datadir=testdatadir,
pairs=pairs,
timeframe='5m',
timerange=timerange)
trades = trades[trades['pair'].isin(pairs)]
fig = generate_profit_graph(pairs,
data,
trades,
timeframe="5m",
stake_currency='BTC')
assert isinstance(fig, go.Figure)
assert fig.layout.title.text == "Freqtrade Profit plot"
assert fig.layout.yaxis.title.text == "Price"
assert fig.layout.yaxis2.title.text == "Profit BTC"
assert fig.layout.yaxis3.title.text == "Profit BTC"
figure = fig.layout.figure
assert len(figure.data) == 7
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scatter)
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 35.69%")
assert isinstance(drawdown, go.Scatter)
parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
assert isinstance(parallel, go.Scatter)
underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
assert isinstance(underwater, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
assert isinstance(profit_pair, go.Scatter)
with pytest.raises(OperationalException, match=r"No trades found.*"):
# Pair cannot be empty - so it's an empty dataframe.
generate_profit_graph(pairs,
data,
trades.loc[trades['pair'].isnull()],
timeframe="5m",
stake_currency='BTC')
def test_generate_profit_graph():
filename = history.make_testdata_path(None) / "backtest-result_test.json"
trades = load_backtest_data(filename)
timerange = Arguments.parse_timerange("20180110-20180112")
pairs = ["POWR/BTC", "XLM/BTC"]
tickers = history.load_data(datadir=None,
pairs=pairs,
ticker_interval='5m',
timerange=timerange)
trades = trades[trades['pair'].isin(pairs)]
fig = generate_profit_graph(pairs, tickers, trades)
assert isinstance(fig, go.Figure)
assert fig.layout.title.text == "Profit plot"
figure = fig.layout.figure
assert len(figure.data) == 4
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scattergl)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scattergl)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
assert isinstance(profit_pair, go.Scattergl)
def test_generate_profit_graph(testdatadir):
filename = testdatadir / "backtest-result_test.json"
trades = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112")
pairs = ["TRX/BTC", "ADA/BTC"]
tickers = history.load_data(datadir=testdatadir,
pairs=pairs,
ticker_interval='5m',
timerange=timerange)
trades = trades[trades['pair'].isin(pairs)]
fig = generate_profit_graph(pairs, tickers, trades, timeframe="5m")
assert isinstance(fig, go.Figure)
assert fig.layout.title.text == "Freqtrade Profit plot"
assert fig.layout.yaxis.title.text == "Price"
assert fig.layout.yaxis2.title.text == "Profit"
assert fig.layout.yaxis3.title.text == "Profit"
figure = fig.layout.figure
assert len(figure.data) == 4
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
assert isinstance(profit_pair, go.Scatter)
def plot_profit(config: Dict[str, Any]) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
# Filter trades to relevant pairs
trades = trades[trades['pair'].isin(plot_elements["pairs"])]
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"],
plot_elements["tickers"], trades)
store_plot_file(fig, filename='freqtrade-profit-plot.html', auto_open=True)
