def get_full_ohlcvs(self):
"""Getting all histrical price data through binance api.
Returns:
A DataFrame of OHLCV data for all.
"""
symbol_lookbacks = self.get_all_symbol_lookback()
ohlcvs = {}
for (symbol, freq), lookback in symbol_lookbacks.items():
ohlcvs[(symbol, freq)] = get_all_binance(symbol, freq)
return ohlcvs
def portfolio_backtest(self, ohlcvs, min_freq, quote_assets=['BTC', 'USDT', 'BUSD', 'USDC'], fee=0.002, delay=0):
"""Display portfolio backtest result.
Calculate overall account asset changes.
Unit is USD
Args:
ohlcvs: A dataframe of symbel.
min_freq: A str of calculation frequency ex('4h').
quote_assets: A list of assets name ex(['BTC', 'USDT', 'BUSD', 'ETH']).
fee: A float of trading fee.
delay: A int of delayed entry and exit setting.
Returns:
widget GUI
"""
# backtest_results
results = []
for method in self._trading_methods:
for symbol in method.symbols:
ohlcv = ohlcvs[(symbol, method.freq)]
result = method.strategy.backtest(ohlcv,
method.variables, filters=method.filters, freq=method.freq)
# find weight_btc if it is in the nested dictionary
weight_btc = method.weight
if isinstance(weight_btc, dict):
weight_btc = (weight_btc[symbol]
if symbol in weight else weight_btc['default'])
weight_btc *= self.ticker_info.get_asset_price_in_btc(method.weight_unit)
results.append({
'name': method.name,
'symbol': symbol,
'freq': method.freq,
'weight': weight_btc,
'portfolio': result,
'trading_method': method,
'signal': result.cash().iloc[-1] == 0,
})
results = pd.DataFrame(results)
import matplotlib.pyplot as plt
position = {}
quote_substract = {}
for index, value in results.transpose().items():
position[value.loc['name'] + '|' + value.symbol + '|' + value.freq] = (value.portfolio.cash() == 0).shift(
delay).ffill() * value.weight
position = pd.DataFrame(position).resample(min_freq).last().ffill()
position.columns = position.columns.str.split('|').str[1]
position = position.ffill().fillna(0)
position = position.groupby(position.columns, axis=1).sum()
# find quote assets
quote_asset_col = []
for symbol in position.columns:
for q in quote_assets:
if symbol[-len(q):] == q:
quote_asset_col.append(q)
break
quote_position = position.copy()
quote_position.columns = quote_asset_col
quote_position = -quote_position.groupby(quote_position.columns, axis=1).sum()
# calculate return in usdt
assets = position.columns.str.split('|').str[0].to_list()
for i, a in enumerate(assets):
for q in quote_assets:
if len(a) > 5 and a[-len(q):] == q:
assets[i] = a[:-len(q)]
position.columns = assets
position = position.groupby(position.columns, axis=1).sum()
quote_position = quote_position.groupby(quote_position.columns, axis=1).sum()
all_symbols = list(set(quote_position.columns) | set(position.columns) | set(self._margins.keys()))
if 'USDT' not in all_symbols:
all_symbols.append('USDT')
position = position.reindex(all_symbols, axis=1).fillna(0) + quote_position.reindex(all_symbols, axis=1).fillna(
0)
ohlcv_usdt = {a: get_all_binance(a + 'USDT', min_freq) for a in position.columns if a != 'USDT'}
initial_margin_sum_btc = 0
for a, w in self._margins.items():
position[a] += self.ticker_info.get_asset_price_in_btc(a) * w
initial_margin_sum_btc += self.ticker_info.get_asset_price_in_btc(a) * w
# remove negative position
negative_position = ((position < 0) * position).drop('USDT', axis=1, errors='ignore').sum(axis=1)
pusdt = position['USDT'].copy()
position = position.clip(0, None)
position.USDT = pusdt + negative_position
addition_usdt = -min(position.USDT.min(), 0) / self.ticker_info.get_asset_price_in_btc('USDT')
if addition_usdt > 0:
print('WARRN**: additional usdt is required: ', addition_usdt, ' USD')
p = position.loc[position.index[(position != position.shift()).abs().sum(axis=1) != 0] | position.index[-1:]]
p.index = p.index.tz_localize(None)
ohlcv_usdt_close = pd.DataFrame({name: s.close for name, s in ohlcv_usdt.items()})
ohlcv_usdt_close.index = ohlcv_usdt_close.index.tz_localize(None)
rebalance_time = (p.index & ohlcv_usdt_close.index)
ohlcv_usdt_close = ohlcv_usdt_close.loc[rebalance_time]
p = p.loc[rebalance_time].fillna(0)
asset_return = ((ohlcv_usdt_close.pct_change().shift(-1).fillna(0)) * p) - fee * (p - p.shift()).abs()
asset_return.fillna(0, inplace=True)
(asset_return.cumsum() / self.ticker_info.get_asset_price_in_btc('USDT')).plot()
plt.show()
s = (asset_return.sum(axis=1).cumsum() + initial_margin_sum_btc) / self.ticker_info.get_asset_price_in_btc(
'USDT')
s.plot()
plt.show()
(s / s.cummax()).plot()
plt.show()
return results