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David Brazda c11ed9d474 Initial commit after copying files from flawed repository

2024-08-30 20:49:53 +02:00

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Pairs trading¶

In [ ]:

from vectorbtpro import *
# whats_imported()

vbt.settings.set_theme("dark")

Selection¶

In [ ]:

SYMBOLS = vbt.BinanceData.list_symbols("*USDT")
POOL_FILE = "temp/data_pool.h5"
START = "2018"
END = "2023"

# vbt.remove_dir("temp", with_contents=True, missing_ok=True)
vbt.make_dir("temp")

if not vbt.file_exists(POOL_FILE):
    with vbt.ProgressBar(total=len(SYMBOLS)) as pbar:
        collected = 0
        for symbol in SYMBOLS:
            try:
                data = vbt.BinanceData.pull(
                    symbol, 
                    start=START,
                    end=END,
                    show_progress=False,
                    silence_warnings=True
                )
                data.to_hdf(POOL_FILE)
                collected += 1
            except Exception:
                pass
            pbar.set_prefix(f"{symbol} ({collected})")
            pbar.update()

In [ ]:

SELECT_START = "2020"
SELECT_END = "2021"

data = vbt.HDFData.pull(
    POOL_FILE, 
    start=SELECT_START, 
    end=SELECT_END, 
    silence_warnings=True
)

print(len(data.symbols))

In [ ]:

data = data.select([
    k 
    for k, v in data.data.items() 
    if not v.isnull().any().any()
])

print(len(data.symbols))

In [ ]:

@vbt.parameterized(
    merge_func="concat", 
    engine="pathos",
    distribute="chunks",
    n_chunks="auto"
)
def coint_pvalue(close, s1, s2):
    import statsmodels.tsa.stattools as ts
    import numpy as np
    return ts.coint(np.log(close[s1]), np.log(close[s2]))[1]

COINT_FILE = "temp/coint_pvalues.pickle"

# vbt.remove_file(COINT_FILE, missing_ok=True)
if not vbt.file_exists(COINT_FILE):
    coint_pvalues = coint_pvalue(
        data.close,
        vbt.Param(data.symbols, condition="s1 != s2"),
        vbt.Param(data.symbols)
    )
    vbt.save(coint_pvalues, COINT_FILE)
else:
    coint_pvalues = vbt.load(COINT_FILE)

In [ ]:

coint_pvalues = coint_pvalues.sort_values()

print(coint_pvalues)

In [ ]:

S1, S2 = "ALGOUSDT", "QTUMUSDT"

data.plot(column="Close", symbol=[S1, S2], base=1).show_svg()

In [ ]:

S1_log = np.log(data.get("Close", S1))
S2_log = np.log(data.get("Close", S2))
log_diff = (S2_log - S1_log).rename("Log diff")
fig = log_diff.vbt.plot()
fig.add_hline(y=log_diff.mean(), line_color="yellow", line_dash="dot")
fig.show_svg()

Testing¶

In [ ]:

DATA_FILE = "temp/data.pickle"

# vbt.remove_file(DATA_FILE, missing_ok=True)
if not vbt.file_exists(DATA_FILE):
    data = vbt.BinanceData.pull(
        [S1, S2], 
        start=SELECT_END,
        end=END, 
        timeframe="hourly"
    )
    vbt.save(data, DATA_FILE)
else:
    data = vbt.load(DATA_FILE)

print(len(data.index))

Level: Researcher¶

In [ ]:

import scipy.stats as st

WINDOW = 24 * 30
UPPER = st.norm.ppf(1 - 0.05 / 2)
LOWER = -st.norm.ppf(1 - 0.05 / 2)

S1_close = data.get("Close", S1)
S2_close = data.get("Close", S2)
ols = vbt.OLS.run(S1_close, S2_close, window=vbt.Default(WINDOW))
spread = ols.error.rename("Spread")
zscore = ols.zscore.rename("Z-score")
print(pd.concat((spread, zscore), axis=1))

In [ ]:

upper_crossed = zscore.vbt.crossed_above(UPPER)
lower_crossed = zscore.vbt.crossed_below(LOWER)

fig = zscore.vbt.plot()
fig.add_hline(y=UPPER, line_color="orangered", line_dash="dot")
fig.add_hline(y=0, line_color="yellow", line_dash="dot")
fig.add_hline(y=LOWER, line_color="limegreen", line_dash="dot")
upper_crossed.vbt.signals.plot_as_exits(zscore, fig=fig)
lower_crossed.vbt.signals.plot_as_entries(zscore, fig=fig)
fig.show_svg()

In [ ]:

long_entries = data.symbol_wrapper.fill(False)
short_entries = data.symbol_wrapper.fill(False)

short_entries.loc[upper_crossed, S1] = True
long_entries.loc[upper_crossed, S2] = True
long_entries.loc[lower_crossed, S1] = True
short_entries.loc[lower_crossed, S2] = True

print(long_entries.sum())
print(short_entries.sum())

In [ ]:

pf = vbt.Portfolio.from_signals(
    data,
    entries=long_entries,
    short_entries=short_entries,
    size=10,
    size_type="valuepercent100",
    group_by=True,
    cash_sharing=True,
    call_seq="auto"
)

In [ ]:

fig = pf.plot_allocations()
rebalancing_dates = data.index[np.unique(pf.orders.idx.values)]
for date in rebalancing_dates:
    fig.add_vline(x=date, line_color="teal", line_dash="dot")
fig.show_svg()

In [ ]:

pf.stats()

In [ ]:

allocations = data.symbol_wrapper.fill()
allocations.loc[upper_crossed, S1] = -0.1
allocations.loc[upper_crossed, S2] = 0.1
allocations.loc[lower_crossed, S1] = 0.1
allocations.loc[lower_crossed, S2] = -0.1
pfo = vbt.PortfolioOptimizer.from_filled_allocations(allocations)
pfo.plot().show_svg()

In [ ]:

print(pfo.allocations)

In [ ]:

pf = pfo.simulate(data, pf_method="from_signals")
pf.total_return

In [ ]:

PTS_expr = """
    PTS:
    x = @in_close.iloc[:, 0]
    y = @in_close.iloc[:, 1]
    ols = vbt.OLS.run(x, y, window=@p_window, hide_params=True)
    upper = st.norm.ppf(1 - @p_upper_alpha / 2)
    lower = -st.norm.ppf(1 - @p_lower_alpha / 2)
    upper_crossed = ols.zscore.vbt.crossed_above(upper)
    lower_crossed = ols.zscore.vbt.crossed_below(lower)
    long_entries = wrapper.fill(False)
    short_entries = wrapper.fill(False)
    short_entries.loc[upper_crossed, x.name] = True
    long_entries.loc[upper_crossed, y.name] = True
    long_entries.loc[lower_crossed, x.name] = True
    short_entries.loc[lower_crossed, y.name] = True
    long_entries, short_entries
"""

PTS = vbt.IF.from_expr(PTS_expr, keep_pd=True, st=st)
vbt.phelp(PTS.run)

In [ ]:

WINDOW_SPACE = np.arange(5, 50).tolist()
ALPHA_SPACE = (np.arange(1, 100) / 1000).tolist()

long_entries, short_entries = data.run(
    PTS, 
    window=WINDOW_SPACE,
    upper_alpha=ALPHA_SPACE,
    lower_alpha=ALPHA_SPACE,
    param_product=True,
    random_subset=1000,
    seed=42,
    unpack=True
)
print(long_entries.columns)

In [ ]:

pf = vbt.Portfolio.from_signals(
    data,
    entries=long_entries,
    short_entries=short_entries,
    size=10,
    size_type="valuepercent100",
    group_by=vbt.ExceptLevel("symbol"),
    cash_sharing=True,
    call_seq="auto"
)

In [ ]:

opt_results = pd.concat((
    pf.total_return,
    pf.trades.expectancy,
), axis=1)
print(opt_results.sort_values(by="total_return", ascending=False))

In [ ]:

best_index = opt_results.idxmax()["expectancy"]
best_long_entries = long_entries[best_index]
best_short_entries = short_entries[best_index]
STOP_SPACE = [np.nan] + np.arange(1, 100).tolist()

pf = vbt.Portfolio.from_signals(
    data,
    entries=best_long_entries,
    short_entries=best_short_entries,
    size=10,
    size_type="valuepercent100",
    group_by=vbt.ExceptLevel("symbol"),
    cash_sharing=True,
    call_seq="auto",
    sl_stop=vbt.Param(STOP_SPACE),
    tsl_stop=vbt.Param(STOP_SPACE),
    tp_stop=vbt.Param(STOP_SPACE),
    delta_format="percent100",
    stop_exit_price="close",
    broadcast_kwargs=dict(random_subset=1000, seed=42)
)

In [ ]:

opt_results = pd.concat((
    pf.total_return,
    pf.trades.expectancy,
), axis=1)
print(opt_results.sort_values(by="total_return", ascending=False))

In [ ]:

def plot_metric_by_stop(stop_name, metric_name, stat_name, smooth):
    from scipy.signal import savgol_filter

    values = pf.deep_getattr(metric_name)
    values = values.vbt.select_levels(stop_name)
    values = getattr(values.groupby(values.index), stat_name)()
    smooth_values = savgol_filter(values, smooth, 1)
    smooth_values = values.vbt.wrapper.wrap(smooth_values)
    fig = values.rename(metric_name).vbt.plot()
    smooth_values.rename(f"{metric_name} (smoothed)").vbt.plot(
        trace_kwargs=dict(line=dict(dash="dot", color="yellow")),
        fig=fig, 
    )
    return fig

plot_metric_by_stop(
    "sl_stop", 
    "trades.expectancy", 
    "median",
    10
).show_svg()
plot_metric_by_stop(
    "tsl_stop", 
    "trades.expectancy", 
    "median",
    10
).show_svg()
plot_metric_by_stop(
    "tp_stop", 
    "trades.expectancy", 
    "median",
    10
).show_svg()

Level: Engineer¶

In [ ]:

@njit(nogil=True)
def pt_signals_nb(close, window=WINDOW, upper=UPPER, lower=LOWER):
    x = np.expand_dims(close[:, 0], 1)
    y = np.expand_dims(close[:, 1], 1)
    _, _, zscore = vbt.ind_nb.ols_nb(x, y, window)
    zscore_1d = zscore[:, 0]
    upper_ts = np.full_like(zscore_1d, upper, dtype=np.float_)
    lower_ts = np.full_like(zscore_1d, lower, dtype=np.float_)
    upper_crossed = vbt.nb.crossed_above_1d_nb(zscore_1d, upper_ts)
    lower_crossed = vbt.nb.crossed_above_1d_nb(lower_ts, zscore_1d)
    long_entries = np.full_like(close, False, dtype=np.bool_)
    short_entries = np.full_like(close, False, dtype=np.bool_)
    short_entries[upper_crossed, 0] = True
    long_entries[upper_crossed, 1] = True
    long_entries[lower_crossed, 0] = True
    short_entries[lower_crossed, 1] = True
    return long_entries, short_entries

In [ ]:

long_entries, short_entries = pt_signals_nb(data.close.values)
long_entries = data.symbol_wrapper.wrap(long_entries)
short_entries = data.symbol_wrapper.wrap(short_entries)

print(long_entries.sum())
print(short_entries.sum())

In [ ]:

@njit(nogil=True)
def pt_portfolio_nb(
    open, 
    high, 
    low, 
    close,
    long_entries,
    short_entries,
    sl_stop=np.nan,
    tsl_stop=np.nan,
    tp_stop=np.nan,
):
    target_shape = close.shape
    group_lens = np.array([2])
    sim_out = vbt.pf_nb.from_signals_nb(
        target_shape=target_shape,
        group_lens=group_lens,
        auto_call_seq=True,
        open=open,
        high=high,
        low=low,
        close=close,
        long_entries=long_entries,
        short_entries=short_entries,
        size=10,
        size_type=vbt.pf_enums.SizeType.ValuePercent100,
        sl_stop=sl_stop,
        tsl_stop=tsl_stop,
        tp_stop=tp_stop,
        delta_format=vbt.pf_enums.DeltaFormat.Percent100,
        stop_exit_price=vbt.pf_enums.StopExitPrice.Close
    )
    return sim_out

In [ ]:

sim_out = pt_portfolio_nb(
    data.open.values,
    data.high.values,
    data.low.values,
    data.close.values,
    long_entries.values,
    short_entries.values
)

In [ ]:

pf = vbt.Portfolio(
    data.symbol_wrapper.regroup(group_by=True),
    sim_out,
    open=data.open,
    high=data.high,
    low=data.low,
    close=data.close,
    cash_sharing=True,
    init_cash=100
)

print(pf.total_return)

In [ ]:

@njit(nogil=True)
def pt_metrics_nb(close, sim_out):
    target_shape = close.shape
    group_lens = np.array([2])
    filled_close = vbt.nb.fbfill_nb(close)
    col_map = vbt.rec_nb.col_map_nb(
        col_arr=sim_out.order_records["col"], 
        n_cols=target_shape[1]
    )
    total_profit = vbt.pf_nb.total_profit_nb(
        target_shape=target_shape,
        close=filled_close,
        order_records=sim_out.order_records,
        col_map=col_map
    )
    total_profit_grouped = vbt.pf_nb.total_profit_grouped_nb(
        total_profit=total_profit,
        group_lens=group_lens,
    )[0]
    total_return = total_profit_grouped / 100
    trade_records = vbt.pf_nb.get_exit_trades_nb(
        order_records=sim_out.order_records, 
        close=filled_close, 
        col_map=col_map
    )
    trade_records = trade_records[
        trade_records["status"] == vbt.pf_enums.TradeStatus.Closed
    ]
    expectancy = vbt.pf_nb.expectancy_reduce_nb(
        pnl_arr=trade_records["pnl"]
    )
    return total_return, expectancy

In [ ]:

pt_metrics_nb(data.close.values, sim_out)

In [ ]:

@njit(nogil=True)
def pt_pipeline_nb(
    open, 
    high, 
    low, 
    close,
    window=WINDOW,
    upper=UPPER,
    lower=LOWER,
    sl_stop=np.nan,
    tsl_stop=np.nan,
    tp_stop=np.nan,
):
    long_entries, short_entries = pt_signals_nb(
        close, 
        window=window, 
        upper=upper, 
        lower=lower
    )
    sim_out = pt_portfolio_nb(
        open,
        high,
        low,
        close,
        long_entries,
        short_entries,
        sl_stop=sl_stop,
        tsl_stop=tsl_stop,
        tp_stop=tp_stop
    )
    return pt_metrics_nb(close, sim_out)

In [ ]:

pt_pipeline_nb(
    data.open.values,
    data.high.values,
    data.low.values,
    data.close.values
)

In [ ]:

%%timeit
pt_pipeline_nb(
    data.open.values,
    data.high.values,
    data.low.values,
    data.close.values
)

In [ ]:

param_pt_pipeline = vbt.parameterized(
    pt_pipeline_nb, 
    merge_func="concat",
    seed=42,
    engine="threadpool",
    chunk_len="auto"
)

In [ ]:

UPPER_SPACE = [st.norm.ppf(1 - x / 2) for x in ALPHA_SPACE]
LOWER_SPACE = [-st.norm.ppf(1 - x / 2) for x in ALPHA_SPACE]
POPT_FILE = "temp/param_opt.pickle"

# vbt.remove_file(POPT_FILE, missing_ok=True)
if not vbt.file_exists(POPT_FILE):
    param_opt = param_pt_pipeline(
        data.open.values,
        data.high.values,
        data.low.values,
        data.close.values,
        window=vbt.Param(WINDOW_SPACE),
        upper=vbt.Param(UPPER_SPACE),
        lower=vbt.Param(LOWER_SPACE)
    )
    vbt.save(param_opt, POPT_FILE)
else:
    param_opt = vbt.load(POPT_FILE)

total_return, expectancy = param_opt

In [ ]:

print(total_return)

In [ ]:

grouped_metric = total_return.groupby(level=["upper", "lower"]).mean()
grouped_metric.vbt.heatmap(
    trace_kwargs=dict(colorscale="RdBu", zmid=0),
    yaxis=dict(autorange="reversed")
).show_svg()

In [ ]:

@njit(nogil=True)
def pt_pipeline_mult_nb(
    n_params: int,
    open:     tp.Array2d, 
    high:     tp.Array2d, 
    low:      tp.Array2d, 
    close:    tp.Array2d,
    window:   tp.FlexArray1dLike = WINDOW,
    upper:    tp.FlexArray1dLike = UPPER,
    lower:    tp.FlexArray1dLike = LOWER,
    sl_stop:  tp.FlexArray1dLike = np.nan,
    tsl_stop: tp.FlexArray1dLike = np.nan,
    tp_stop:  tp.FlexArray1dLike = np.nan,
):
    window_ = vbt.to_1d_array_nb(np.asarray(window))
    upper_ = vbt.to_1d_array_nb(np.asarray(upper))
    lower_ = vbt.to_1d_array_nb(np.asarray(lower))
    sl_stop_ = vbt.to_1d_array_nb(np.asarray(sl_stop))
    tsl_stop_ = vbt.to_1d_array_nb(np.asarray(tsl_stop))
    tp_stop_ = vbt.to_1d_array_nb(np.asarray(tp_stop))

    total_return = np.empty(n_params, dtype=np.float_)
    expectancy = np.empty(n_params, dtype=np.float_)

    for i in range(n_params):
        total_return[i], expectancy[i] = pt_pipeline_nb(
            open,
            high,
            low,
            close,
            window=vbt.flex_select_1d_nb(window_, i),
            upper=vbt.flex_select_1d_nb(upper_, i),
            lower=vbt.flex_select_1d_nb(lower_, i),
            sl_stop=vbt.flex_select_1d_nb(sl_stop_, i),
            tsl_stop=vbt.flex_select_1d_nb(tsl_stop_, i),
            tp_stop=vbt.flex_select_1d_nb(tp_stop_, i),
        )
    return total_return, expectancy

In [ ]:

pt_pipeline_mult_nb(
    3,
    data.open.values,
    data.high.values,
    data.low.values,
    data.close.values,
    window=np.array([10, 20, 30])
)

In [ ]:

chunked_pt_pipeline = vbt.chunked(
    pt_pipeline_mult_nb,
    size=vbt.ArgSizer(arg_query="n_params"),
    arg_take_spec=dict(
        n_params=vbt.CountAdapter(),
        open=None,
        high=None,
        low=None,
        close=None,
        window=vbt.FlexArraySlicer(),
        upper=vbt.FlexArraySlicer(),
        lower=vbt.FlexArraySlicer(),
        sl_stop=vbt.FlexArraySlicer(),
        tsl_stop=vbt.FlexArraySlicer(),
        tp_stop=vbt.FlexArraySlicer()
    ),
    chunk_len=1000,
    merge_func="concat",
    execute_kwargs=dict(
        chunk_len="auto",
        engine="threadpool",
    )
)

In [ ]:

param_product, param_index = vbt.combine_params(
    dict(
        window=vbt.Param(WINDOW_SPACE),
        upper=vbt.Param(UPPER_SPACE),
        lower=vbt.Param(LOWER_SPACE)
    )
)

In [ ]:

COPT_FILE = "temp/chunked_opt.pickle"

# vbt.remove_file(COPT_FILE, missing_ok=True)
if not vbt.file_exists(COPT_FILE):
    chunked_opt = chunked_pt_pipeline(
        len(param_index),
        data.open.values,
        data.high.values,
        data.low.values,
        data.close.values,
        window=param_product["window"],
        upper=param_product["upper"],
        lower=param_product["lower"]
    )
    vbt.save(chunked_opt, COPT_FILE)
else:
    chunked_opt = vbt.load(COPT_FILE)

total_return, expectancy = chunked_opt

In [ ]:

total_return = pd.Series(total_return, index=param_index)
expectancy = pd.Series(expectancy, index=param_index)

In [ ]:

GRID_LEN = len(WINDOW_SPACE) * \
    len(UPPER_SPACE) * \
    len(LOWER_SPACE) * \
    len(STOP_SPACE) ** 3
print(GRID_LEN)

In [ ]:

GRID = dict(
    window=WINDOW_SPACE,
    upper=UPPER_SPACE,
    lower=LOWER_SPACE,
    sl_stop=STOP_SPACE,
    tsl_stop=STOP_SPACE,
    tp_stop=STOP_SPACE,
)
vbt.pprint(vbt.pick_from_param_grid(GRID, 123_456_789))

In [ ]:

FOUND_FILE = "temp/found.pickle"
BEST_N = 100
BEST_TH = 1.0
CHUNK_LEN = 10_000

# vbt.remove_file(FOUND_FILE, missing_ok=True)
if vbt.file_exists(FOUND_FILE):
    found = vbt.load(FOUND_FILE)
else:
    found = None
with (
    vbt.ProgressBar(
        desc="Found", 
        initial=0 if found is None else len(found),
        total=BEST_N
    ) as pbar1,
    vbt.ProgressBar(
        desc="Processed"
    ) as pbar2
):
    while found is None or len(found) < BEST_N:
        param_df = pd.DataFrame([
            vbt.pick_from_param_grid(GRID) 
            for _ in range(CHUNK_LEN)
        ])
        param_index = pd.MultiIndex.from_frame(param_df)
        _, expectancy = chunked_pt_pipeline(
            CHUNK_LEN,
            data.open.values,
            data.high.values,
            data.low.values,
            data.close.values,
            window=param_df["window"],
            upper=param_df["upper"],
            lower=param_df["lower"],
            sl_stop=param_df["sl_stop"],
            tsl_stop=param_df["tsl_stop"],
            tp_stop=param_df["tp_stop"],
            _chunk_len=None,
            _execute_kwargs=dict(
                chunk_len=None
            )
        )
        expectancy = pd.Series(expectancy, index=param_index)
        best_mask = expectancy >= BEST_TH
        if best_mask.any():
            best = expectancy[best_mask]
            if found is None:
                found = best
            else:
                found = pd.concat((found, best))
                found = found[~found.index.duplicated(keep="first")]
            vbt.save(found, FOUND_FILE)
            pbar1.update_to(len(found))
            pbar1.refresh()
        pbar2.update(len(expectancy))

In [ ]:

def get_param_median(param):
    return found.index.get_level_values(param).to_series().median()

pt_pipeline_nb(
    data.open.values, 
    data.high.values, 
    data.low.values, 
    data.close.values,
    window=int(get_param_median("window")),
    upper=get_param_median("upper"),
    lower=get_param_median("lower"),
    sl_stop=get_param_median("sl_stop"),
    tsl_stop=get_param_median("tsl_stop"),
    tp_stop=get_param_median("tp_stop")
)

In [ ]:

import optuna

optuna.logging.disable_default_handler()
optuna.logging.set_verbosity(optuna.logging.WARNING)

def objective(trial):
    window = trial.suggest_categorical("window", WINDOW_SPACE)
    upper = trial.suggest_categorical("upper", UPPER_SPACE)
    lower = trial.suggest_categorical("lower", LOWER_SPACE)
    sl_stop = trial.suggest_categorical("sl_stop", STOP_SPACE)
    tsl_stop = trial.suggest_categorical("tsl_stop", STOP_SPACE)
    tp_stop = trial.suggest_categorical("tp_stop", STOP_SPACE)
    total_return, expectancy = pt_pipeline_nb(
        data.open.values,
        data.high.values,
        data.low.values,
        data.close.values,
        window=window,
        upper=upper,
        lower=lower,
        sl_stop=sl_stop,
        tsl_stop=tsl_stop,
        tp_stop=tp_stop
    )
    if np.isnan(total_return):
        raise optuna.TrialPruned()
    if np.isnan(expectancy):
        raise optuna.TrialPruned()
    return total_return, expectancy

study = optuna.create_study(directions=["maximize", "maximize"])
study.optimize(objective, n_trials=1000)

trials_df = study.trials_dataframe(attrs=["params", "values"])
trials_df.set_index([
    "params_window", 
    "params_upper", 
    "params_lower",
    "params_sl_stop",
    "params_tsl_stop",
    "params_tp_stop"
], inplace=True)
trials_df.index.rename([
    "window", 
    "upper", 
    "lower",
    "sl_stop",
    "tsl_stop",
    "tp_stop"
], inplace=True)
trials_df.columns = ["total_return", "expectancy"]
trials_df = trials_df[~trials_df.index.duplicated(keep="first")]
print(trials_df.sort_values(by="total_return", ascending=False))

Level: Architect¶

In [ ]:

InOutputs = namedtuple("InOutputs", ["spread", "zscore"])

@njit(nogil=True, boundscheck=True)
def can_execute_nb(c, wait_days):
    if c.order_counts[c.col] == 0:
        return True
    last_order = c.order_records[c.order_counts[c.col] - 1, c.col]
    ns_delta = c.index[c.i] - c.index[last_order.idx]
    if ns_delta >= wait_days * vbt.dt_nb.d_ns:
        return True
    return False

@njit(nogil=True, boundscheck=True)
def create_signals_nb(c, upper, lower, wait_days):
    _upper = vbt.pf_nb.select_nb(c, upper)
    _lower = vbt.pf_nb.select_nb(c, lower)
    _wait_days = vbt.pf_nb.select_nb(c, wait_days)

    if c.i > 0:
        prev_zscore = c.in_outputs.zscore[c.i - 1, c.group]
        zscore = c.in_outputs.zscore[c.i, c.group]
        if prev_zscore < _upper and zscore > _upper:
            if can_execute_nb(c, _wait_days):
                if c.col % 2 == 0:
                    return False, False, True, False
                return True, False, False, False
        if prev_zscore > _lower and zscore < _lower:
            if can_execute_nb(c, _wait_days):
                if c.col % 2 == 0:
                    return True, False, False, False
                return False, False, True, False
    return False, False, False, False

@njit(nogil=True, boundscheck=True)
def signal_func_nb(c, window, upper, lower, wait_days):
    _window = vbt.pf_nb.select_nb(c, window)
        
    if c.col % 2 == 0:
        x = vbt.pf_nb.select_nb(c, c.close, col=c.col)
        y = vbt.pf_nb.select_nb(c, c.close, col=c.col + 1)
        c.in_outputs.spread[c.i, c.group] = np.log(y) - np.log(x)
        
        window_start = c.i - _window + 1
        window_end = c.i + 1
        if window_start >= 0:
            s = c.in_outputs.spread[window_start : window_end, c.group]
            s_mean = np.nanmean(s)
            s_std = np.nanstd(s)
            c.in_outputs.zscore[c.i, c.group] = (s[-1] - s_mean) / s_std
    return create_signals_nb(c, upper, lower, wait_days)

In [ ]:

WAIT_DAYS = 30

def iter_pt_portfolio(
    window=WINDOW, 
    upper=UPPER, 
    lower=LOWER, 
    wait_days=WAIT_DAYS,
    signal_func_nb=signal_func_nb,
    more_signal_args=(),
    **kwargs
):
    return vbt.Portfolio.from_signals(
        data,
        broadcast_named_args=dict(
            window=window,
            upper=upper,
            lower=lower,
            wait_days=wait_days
        ),
        in_outputs=vbt.RepEval("""
            InOutputs(
                np.full((target_shape[0], target_shape[1] // 2), np.nan), 
                np.full((target_shape[0], target_shape[1] // 2), np.nan)
            )
        """, context=dict(InOutputs=InOutputs)),
        signal_func_nb=signal_func_nb,
        signal_args=(
            vbt.Rep("window"),
            vbt.Rep("upper"),
            vbt.Rep("lower"),
            vbt.Rep("wait_days"),
            *more_signal_args
        ),
        size=10,
        size_type="valuepercent100",
        group_by=vbt.ExceptLevel("symbol"),
        cash_sharing=True,
        call_seq="auto",
        delta_format="percent100",
        stop_exit_price="close",
        **kwargs
    )

In [ ]:

pf = iter_pt_portfolio()

In [ ]:

fig = vbt.make_subplots(
    rows=2, 
    cols=1, 
    vertical_spacing=0,
    shared_xaxes=True
)
zscore = pf.get_in_output("zscore").rename("Z-score")
zscore.vbt.plot(
    add_trace_kwargs=dict(row=1, col=1),
    fig=fig
)
fig.add_hline(row=1, y=UPPER, line_color="orangered", line_dash="dot")
fig.add_hline(row=1, y=0, line_color="yellow", line_dash="dot")
fig.add_hline(row=1, y=LOWER, line_color="limegreen", line_dash="dot")
orders = pf.orders.regroup(group_by=False).iloc[:, 0]
exit_mask = orders.side_sell.get_pd_mask(idx_arr="signal_idx")
entry_mask = orders.side_buy.get_pd_mask(idx_arr="signal_idx")
upper_crossed = zscore.vbt.crossed_above(UPPER)
lower_crossed = zscore.vbt.crossed_below(LOWER)
(upper_crossed & ~exit_mask).vbt.signals.plot_as_exits(
    pf.get_in_output("zscore"),
    trace_kwargs=dict(
        name="Exits (ignored)", 
        marker=dict(color="lightgray"), 
        opacity=0.5
    ),
    add_trace_kwargs=dict(row=1, col=1),
    fig=fig
)
(lower_crossed & ~entry_mask).vbt.signals.plot_as_entries(
    pf.get_in_output("zscore"),
    trace_kwargs=dict(
        name="Entries (ignored)", 
        marker=dict(color="lightgray"), 
        opacity=0.5
    ),
    add_trace_kwargs=dict(row=1, col=1),
    fig=fig
)
exit_mask.vbt.signals.plot_as_exits(
    pf.get_in_output("zscore"),
    add_trace_kwargs=dict(row=1, col=1),
    fig=fig
)
entry_mask.vbt.signals.plot_as_entries(
    pf.get_in_output("zscore"),
    add_trace_kwargs=dict(row=1, col=1),
    fig=fig
)
pf.plot_allocations(
    add_trace_kwargs=dict(row=2, col=1),
    fig=fig
)
rebalancing_dates = data.index[np.unique(orders.idx.values)]
for date in rebalancing_dates:
    fig.add_vline(row=2, x=date, line_color="teal", line_dash="dot")
fig.update_layout(height=600)
fig.show_svg()

In [ ]:

WAIT_SPACE = np.arange(30, 370, 5).tolist()

pf = iter_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))
pf.orders.count().vbt.scatterplot(
    xaxis_title="Wait days",
    yaxis_title="Order count"
).show_svg()

In [ ]:

with (vbt.Timer() as timer, vbt.MemTracer() as tracer):
    iter_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))
print(timer.elapsed())
print(tracer.peak_usage())

In [ ]:

zscore_state_dt = np.dtype(
    [
        ("cumsum", np.float_),
        ("cumsum_sq", np.float_),
        ("nancnt", np.int_)
    ],
    align=True,
)

@njit(nogil=True, boundscheck=True)
def stream_signal_func_nb(
    c, 
    window, 
    upper, 
    lower, 
    wait_days, 
    zscore_state
):
    _window = vbt.pf_nb.select_nb(c, window)
        
    if c.col % 2 == 0:
        x = vbt.pf_nb.select_nb(c, c.close, col=c.col)
        y = vbt.pf_nb.select_nb(c, c.close, col=c.col + 1)
        c.in_outputs.spread[c.i, c.group] = np.log(y) - np.log(x)
        
        value = c.in_outputs.spread[c.i, c.group]
        pre_i = c.i - _window
        if pre_i >= 0:
            pre_window_value = c.in_outputs.spread[pre_i, c.group]
        else:
            pre_window_value = np.nan
        zscore_in_state = vbt.enums.RollZScoreAIS(
            i=c.i,
            value=value,
            pre_window_value=pre_window_value,
            cumsum=zscore_state["cumsum"][c.group],
            cumsum_sq=zscore_state["cumsum_sq"][c.group],
            nancnt=zscore_state["nancnt"][c.group],
            window=_window,
            minp=_window,
            ddof=0
        )
        zscore_out_state = vbt.nb.rolling_zscore_acc_nb(zscore_in_state)
        c.in_outputs.zscore[c.i, c.group] = zscore_out_state.value
        zscore_state["cumsum"][c.group] = zscore_out_state.cumsum
        zscore_state["cumsum_sq"][c.group] = zscore_out_state.cumsum_sq
        zscore_state["nancnt"][c.group] = zscore_out_state.nancnt
        
    return create_signals_nb(c, upper, lower, wait_days)

In [ ]:

from functools import partial

stream_pt_portfolio = partial(
    iter_pt_portfolio,
    signal_func_nb=stream_signal_func_nb,
    more_signal_args=(
        vbt.RepEval(
            """
            zscore_state = np.empty(target_shape[1] // 2, dtype=zscore_state_dt)
            zscore_state["cumsum"] = 0.0
            zscore_state["cumsum_sq"] = 0.0
            zscore_state["nancnt"] = 0
            zscore_state
            """, 
            context=dict(zscore_state_dt=zscore_state_dt)
        ),
    )
)

In [ ]:

stream_pf = stream_pt_portfolio()
print(stream_pf.total_return)

pf = iter_pt_portfolio()
print(pf.total_return)

In [ ]:

stream_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))  # compilation
with (vbt.Timer() as timer, vbt.MemTracer() as tracer):
    stream_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))
print(timer.elapsed())
print(tracer.peak_usage())

In [ ]:

chunked_stream_pt_portfolio = partial(
    stream_pt_portfolio,
    chunked=dict(
        engine="threadpool",
        arg_take_spec=dict(
            signal_args=vbt.ArgsTaker(
                vbt.flex_array_gl_slicer,
                vbt.flex_array_gl_slicer,
                vbt.flex_array_gl_slicer,
                vbt.flex_array_gl_slicer,
                vbt.ArraySlicer(axis=0)
            ),
            in_outputs=vbt.SequenceTaker([
                vbt.ArraySlicer(axis=1),
                vbt.ArraySlicer(axis=1)
            ])
        )
    )
)

In [ ]:

chunked_stream_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))  # compilation
with (vbt.Timer() as timer, vbt.MemTracer() as tracer):
    chunked_stream_pt_portfolio(wait_days=vbt.Param(WAIT_SPACE))

print(timer.elapsed())
print(tracer.peak_usage())

In [ ]:

44 KiB Raw Blame History

Pairs trading¶

Selection¶

Testing¶

Level: Researcher¶

Level: Engineer¶

Level: Architect¶

44 KiB

Raw Blame History