TSMarbell Python Library

from tsbarbell import (
    TSBarbellClient,
    plot_slope_probability,
    plot_break_up_probability
)

# Initialize the client
client = TSBarbellClient(api_key='YOUR_API_KEY')

# Fetch BTC Slope Probability (Trend)
slope_data = client.btc_slope.get_all_horizons(limit=1000)
plot_slope_probability(slope_data).show()

# Fetch BTC Break-Up Probability (Volatility)
break_up_data = client.btc_break_up.get_all_horizons(limit=1000)
plot_break_up_probability(break_up_data).show()

# Fetch Combined Data for Backtesting
df = client.get_combined_data([
    ('btc_slope', 4),
    ('btc_break_up', 1)
], limit=2000)

from tsbarbell import StatisticalTests, run_backtest, plot_statistical_analysis

# Define your entry and exit signals based on indicators
entry_signal = (df['slope_4h'] > 0.9) & (df['break_up_24h'] > 0.65)
exit_signal = (df['slope_4h'] < 0.5) | (df['break_up_24h'] < 0.35)

# Run Backtest
portfolio = run_backtest(df['close'], entry_signal, exit_signal)

# Run Statistical Tests
mc_result = StatisticalTests.monte_carlo_test(
    close_price=df['close'],
    actual_return=portfolio.total_return() * 100,
    entry_signal=entry_signal,
    exit_signal=exit_signal
)

# Visualize Results Dashboard
fig = plot_statistical_analysis(
    df=df,
    portfolio=portfolio,
    test_results=[mc_result],
    metrics={"total_return": portfolio.total_return() * 100}
)
fig.show()