strategy-lab/to_explore/notebooks/BasicRSI.ipynb

Basic RSI strategy¶

Single backtest¶

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from vectorbtpro import *
# whats_imported()

vbt.settings.set_theme('dark')

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data = vbt.BinanceData.pull('BTCUSDT')
data

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data.data['BTCUSDT'].vbt.ohlcv.plot().show_svg()

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data.data['BTCUSDT'].info()

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open_price = data.get('Open')
close_price = data.get('Close')

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vbt.IF.list_indicators("RSI*")

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vbt.indicator("talib:RSI")

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vbt.RSI

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vbt.talib('RSI')

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vbt.ta('RSIIndicator')

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vbt.pandas_ta('RSI')

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print(vbt.format_func(vbt.RSI.run))

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rsi = vbt.RSI.run(open_price)
rsi

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rsi.rsi

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entries = rsi.rsi.vbt.crossed_below(30)
entries

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exits = rsi.rsi.vbt.crossed_above(70)
exits

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entries = rsi.rsi_crossed_below(30)
exits = rsi.rsi_crossed_above(70)

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def plot_rsi(rsi, entries, exits):
    fig = rsi.plot()
    entries.vbt.signals.plot_as_entries(rsi.rsi, fig=fig)
    exits.vbt.signals.plot_as_exits(rsi.rsi, fig=fig)
    return fig

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plot_rsi(rsi, entries, exits).show_svg()

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clean_entries, clean_exits = entries.vbt.signals.clean(exits)

plot_rsi(rsi, clean_entries, clean_exits).show_svg()

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clean_entries.vbt.signals.total()

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clean_exits.vbt.signals.total()

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ranges = clean_entries.vbt.signals.between_ranges(target=clean_exits)
ranges.duration.mean(wrap_kwargs=dict(to_timedelta=True))

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pf = vbt.Portfolio.from_signals(
    close=close_price, 
    entries=clean_entries, 
    exits=clean_exits,
    size=100,
    size_type='value',
    init_cash='auto'
)
pf

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pf.stats()

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pf.plot(settings=dict(bm_returns=False)).show_svg()

Multiple backtests¶

Using for-loop¶

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def test_rsi(window=14, wtype="wilder", lower_th=30, upper_th=70):
    rsi = vbt.RSI.run(open_price, window=window, wtype=wtype)
    entries = rsi.rsi_crossed_below(lower_th)
    exits = rsi.rsi_crossed_above(upper_th)
    pf = vbt.Portfolio.from_signals(
        close=close_price, 
        entries=entries, 
        exits=exits,
        size=100,
        size_type='value',
        init_cash='auto')
    return pf.stats([
        'total_return', 
        'total_trades', 
        'win_rate', 
        'expectancy'
    ])

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test_rsi()

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test_rsi(lower_th=20, upper_th=80)

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from itertools import product

lower_ths = range(20, 31)
upper_ths = range(70, 81)
th_combs = list(product(lower_ths, upper_ths))
len(th_combs)

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comb_stats = [
    test_rsi(lower_th=lower_th, upper_th=upper_th)
    for lower_th, upper_th in th_combs
]

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comb_stats_df = pd.DataFrame(comb_stats)
print(comb_stats_df)

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comb_stats_df.index = pd.MultiIndex.from_tuples(
    th_combs, 
    names=['lower_th', 'upper_th'])
print(comb_stats_df)

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comb_stats_df['Expectancy'].vbt.heatmap().show_svg()

Using columns¶

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windows = list(range(8, 21))
wtypes = ["simple", "exp", "wilder"]
lower_ths = list(range(20, 31))
upper_ths = list(range(70, 81))

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rsi = vbt.RSI.run(
    open_price, 
    window=windows, 
    wtype=wtypes, 
    param_product=True)
rsi.rsi.columns

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lower_ths_prod, upper_ths_prod = zip(*product(lower_ths, upper_ths))
len(lower_ths_prod)

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len(upper_ths_prod)

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lower_th_index = vbt.Param(lower_ths_prod, name='lower_th')
entries = rsi.rsi_crossed_below(lower_th_index)
entries.columns

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upper_th_index = vbt.Param(upper_ths_prod, name='upper_th')
exits = rsi.rsi_crossed_above(upper_th_index)
exits.columns

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pf = vbt.Portfolio.from_signals(
    close=close_price, 
    entries=entries, 
    exits=exits,
    size=100,
    size_type='value',
    init_cash='auto'
)
pf

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stats_df = pf.stats([
    'total_return', 
    'total_trades', 
    'win_rate', 
    'expectancy'
], agg_func=None)
print(stats_df)

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>>> print(pf.getsize())

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>>> np.product(pf.wrapper.shape) * 8 / 1024 / 1024

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stats_df['Expectancy'].groupby('rsi_window').mean()

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print(stats_df.sort_values(by='Expectancy', ascending=False).head())

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pf[(22, 80, 20, "wilder")].plot_value().show_svg()

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data = vbt.BinanceData.pull(['BTCUSDT', 'ETHUSDT'])

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open_price = data.get('Open')
close_price = data.get('Close')

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rsi = vbt.RSI.run(
    open_price, 
    window=windows, 
    wtype=wtypes, 
    param_product=True)
entries = rsi.rsi_crossed_below(lower_th_index)
exits = rsi.rsi_crossed_above(upper_th_index)
pf = vbt.Portfolio.from_signals(
    close=close_price, 
    entries=entries, 
    exits=exits,
    size=100,
    size_type='value',
    init_cash='auto'
)
stats_df = pf.stats([
    'total_return', 
    'total_trades', 
    'win_rate', 
    'expectancy'
], agg_func=None)

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stats_df.index

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eth_mask = stats_df.index.get_level_values('symbol') == 'ETHUSDT'
btc_mask = stats_df.index.get_level_values('symbol') == 'BTCUSDT'
pd.DataFrame({
    'ETHUSDT': stats_df[eth_mask]['Expectancy'].values,
    'BTCUSDT': stats_df[btc_mask]['Expectancy'].values
}).vbt.histplot(xaxis=dict(title="Expectancy")).show_svg()

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