def test_backtest(conf, pairs, mocker):
trades = []
mocker.patch.dict('freqtrade.main._CONF', conf)
for pair in pairs:
with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
data = json.load(data_file)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
ticker = analyze_ticker(pair)
# for each buy point
for index, row in ticker[ticker.buy == 1].iterrows():
trade = Trade(
open_rate=row['close'],
open_date=arrow.get(row['date']).datetime,
amount=1,
)
# calculate win/lose forwards from buy point
for index2, row2 in ticker[index:].iterrows():
if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
trades.append((pair, current_profit, index2 - index))
break
labels = ['currency', 'profit', 'duration']
results = DataFrame.from_records(trades, columns=labels)
print('====================== BACKTESTING REPORT ================================')
for pair in pairs:
print('For currency {}:'.format(pair))
print_results(results[results.currency == pair])
print('TOTAL OVER ALL TRADES:')
print_results(results)
def backtest(backtest_conf, backdata, mocker):
trades = []
exchange._API = Bittrex({'key': '', 'secret': ''})
mocked_history = mocker.patch('freqtrade.analyze.get_ticker_history')
mocker.patch.dict('freqtrade.main._CONF', backtest_conf)
mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
for pair, pair_data in backdata.items():
mocked_history.return_value = pair_data
ticker = analyze_ticker(pair)[['close', 'date', 'buy']].copy()
# for each buy point
for row in ticker[ticker.buy == 1].itertuples(index=True):
trade = Trade(open_rate=row.close,
open_date=row.date,
amount=1,
fee=exchange.get_fee() * 2)
# calculate win/lose forwards from buy point
for row2 in ticker[row.Index:].itertuples(index=True):
if should_sell(trade, row2.close, row2.date):
current_profit = trade.calc_profit(row2.close)
trades.append(
(pair, current_profit, row2.Index - row.Index))
break
labels = ['currency', 'profit', 'duration']
results = DataFrame.from_records(trades, columns=labels)
return results
def plot_analyzed_dataframe(args) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
pair = args.pair
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
def plot_analyzed_dataframe(args) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
pair = args.pair
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
def backtest(conf, pairs, mocker, buy_strategy):
trades = []
mocker.patch.dict('freqtrade.main._CONF', conf)
for pair in pairs:
with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
data = json.load(data_file)
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
ticker = analyze_ticker(pair)
# for each buy point
for index, row in ticker[ticker.buy == 1].iterrows():
trade = Trade(
open_rate=row['close'],
open_date=arrow.get(row['date']).datetime,
amount=1,
)
# calculate win/lose forwards from buy point
for index2, row2 in ticker[index:].iterrows():
if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
trades.append((pair, current_profit, index2 - index))
break
labels = ['currency', 'profit', 'duration']
results = DataFrame.from_records(trades, columns=labels)
print_results(results)
# set the value below to suit your number concurrent trades so its realistic to 20days of data
TARGET_TRADES = 1200
if results.profit.sum() == 0 or results.profit.mean() == 0:
return 49999999999 # avoid division by zero, return huge value to discard result
return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
# ensure directory exists
base_path = os.path.join(os.path.expanduser('~'), 'freqtrade')
data_path = os.path.join(base_path, 'ml_dev', 'data', 'markets')
market_path = os.path.join(data_path, market)
if not os.path.isdir(market_path):
os.mkdir(market_path)
# get configuration
with open(os.path.join(base_path, 'config.json')) as file:
_CONF = json.load(file)
# initialize the exchange
exchange.init(_CONF)
# get ticker hisroty
df = analyze_ticker(pair=market, tick_interval=time_interval)
# remove first 100 rows to allow ema100
df = df[100:]
df.to_csv('test.csv', index=False)
master = pd.read_csv('test.csv')
# get targets
master['percent_change'] = 0.
print(master.shape)
for entry in range(len(master) - 1):
print(entry)