Running Catalyst in Jupyter Notebook

The Jupyter Notebook is a very powerful browser-based interface to a Python interpreter. As it is already the de-facto interface for most quantitative researchers, catalyst provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI.

To use it you have to write your algorithm in a cell and let catalyst know that it is supposed to run this algorithm. This is done via the %%catalyst IPython magic command that is available after you import catalyst from within the Notebook. This magic takes the same arguments as the command line interface. Thus, to run the algorithm just supply the same parameters as the CLI but without the -f and -o arguments. We just have to execute the following cell after importing catalyst to register the magic.

Please remember to ingest the data that you need from the console since that functionality is not supported from within the Notebook. For the example below, you first need to run the following from the CLI:

catalyst ingest-exchange -x bitfinex -i btc_usd

In [1]:

# Register the catalyst magic
%load_ext catalyst

In [2]:

# Setup matplotlib to display graphs inline in this Notebook
%matplotlib inline

Note below that we do not have to specify an input file (-f) since the magic will use the contents of the cell and look for your algorithm functions.

In [5]:

%%catalyst --start 2015-3-1 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd

from catalyst.finance.slippage import VolumeShareSlippage

from catalyst.api import (
    order_target_value,
    symbol,
    record,
    cancel_order,
    get_open_orders,
)

def initialize(context):
    context.ASSET_NAME = 'btc_usd'
    context.TARGET_HODL_RATIO = 0.8
    context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO

    # For all trading pairs in the poloniex bundle, the default denomination
    # currently supported by Catalyst is 1/1000th of a full coin. Use this
    # constant to scale the price of up to that of a full coin if desired.
    context.TICK_SIZE = 1000.0

    context.is_buying = True
    context.asset = symbol(context.ASSET_NAME)

    context.i = 0

def handle_data(context, data):
    context.i += 1

    starting_cash = context.portfolio.starting_cash
    target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
    reserve_value = context.RESERVE_RATIO * starting_cash

    # Cancel any outstanding orders
    orders = get_open_orders(context.asset) or []
    for order in orders:
        cancel_order(order)
    
    # Stop buying after passing the reserve threshold
    cash = context.portfolio.cash
    if cash <= reserve_value:
        context.is_buying = False

    # Retrieve current asset price from pricing data
    price = data.current(context.asset,'price')

    # Check if still buying and could (approximately) afford another purchase
    if context.is_buying and cash > price:
        # Place order to make position in asset equal to target_hodl_value
        order_target_value(
            context.asset,
            target_hodl_value,
            limit_price=price*1.1,
        )

    record(
        price=price,
        volume=data.current(context.asset,'volume'),
        cash=cash,
        starting_cash=context.portfolio.starting_cash,
        leverage=context.account.leverage,
    )

def analyze(context=None, results=None):
    import matplotlib.pyplot as plt

    # Plot the portfolio and asset data.
    ax1 = plt.subplot(611)
    results[['portfolio_value']].plot(ax=ax1)
    ax1.set_ylabel('Portfolio Value (USD)')

    ax2 = plt.subplot(612, sharex=ax1)
    ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
    (context.TICK_SIZE * results[['price']]).plot(ax=ax2)

    trans = results.ix[[t != [] for t in results.transactions]]
    buys = trans.ix[
        [t[0]['amount'] > 0 for t in trans.transactions]
    ]
    ax2.plot(
        buys.index,
        context.TICK_SIZE * results.price[buys.index],
        '^',
        markersize=10,
        color='g',
    )

    ax3 = plt.subplot(613, sharex=ax1)
    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
    ax3.set_ylabel('Leverage ')

    ax4 = plt.subplot(614, sharex=ax1)
    results[['starting_cash', 'cash']].plot(ax=ax4)
    ax4.set_ylabel('Cash (USD)')

    results[[
        'treasury',
        'algorithm',
        'benchmark',
    ]] = results[[
        'treasury_period_return',
        'algorithm_period_return',
        'benchmark_period_return',
    ]]

    ax5 = plt.subplot(615, sharex=ax1)
    results[[
        'treasury',
        'algorithm',
        'benchmark',
    ]].plot(ax=ax5)
    ax5.set_ylabel('Percent Change')

    ax6 = plt.subplot(616, sharex=ax1)
    results[['volume']].plot(ax=ax6)
    ax6.set_ylabel('Volume (mCoins/5min)')

    plt.legend(loc=3)

    # Show the plot.
    plt.gcf().set_size_inches(18, 8)
    plt.show()

Out [5]:

[2017-12-12 23:24:22.056836] INFO: run_algo: running algo in backtest mode
[2017-12-12 23:24:22.138743] INFO: exchange_bundle: pricing data for [u'btc_usdt'] not found in range 2015-03-01 00:00:00+00:00 to 2017-06-27 00:00:00+00:00, updating the bundles.

Ingesting daily price data for btc_usdt on poloniex

[2017-12-12 23:24:23.713668] INFO: exchange_algorithm: initialized trading algorithm in backtest mode
[2017-12-12 23:24:27.865042] INFO: Performance: Simulated 851 trading days out of 851.
[2017-12-12 23:24:27.865826] INFO: Performance: first open: 2015-03-01 00:00:00+00:00
[2017-12-12 23:24:27.866537] INFO: Performance: last close: 2017-06-28 23:59:00+00:00

	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	cash	ending_cash	ending_exposure	...	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return	volume	treasury	algorithm	benchmark
2015-03-01 23:59:00+00:00	NaN	0.000000	NaN	0.000000	NaN	NaN	0.00000	100000.00000	100000.00000	0.000000	...	100000.0	0.000000	0.000000	1	[]	0.0200	47597.424668	0.0200	0.000000	0.000000
2015-03-02 23:59:00+00:00	0.024686	-0.002199	0.000000	0.071713	0.804978	-0.030667	-84796.13883	15203.86117	15203.86117	84576.219515	...	100000.0	0.000000	0.000000	2	[{u'commission': None, u'amount': 304.87804879...	0.0208	67168.440498	0.0208	-0.002199	0.071713
2015-03-03 23:59:00+00:00	0.188434	0.017130	2.396606	0.064622	0.689588	-0.174486	0.00000	15203.86117	15203.86117	86509.146345	...	100000.0	84576.219515	84576.219515	3	[]	0.0212	81226.398297	0.0212	0.017130	0.064622
2015-03-04 23:59:00+00:00	0.331241	-0.014638	1.409251	0.115538	0.600022	-0.322610	0.00000	15203.86117	15203.86117	83332.317076	...	100000.0	86509.146345	86509.146345	4	[]	0.0212	71521.587766	0.0212	-0.014638	0.115538
2015-03-05 23:59:00+00:00	0.311232	-0.001346	0.290119	0.023905	0.940214	-0.205552	0.00000	15203.86117	15203.86117	84661.585369	...	100000.0	83332.317076	83332.317076	5	[]	0.0211	66108.884634	0.0211	-0.001346	0.023905
2015-03-06 23:59:00+00:00	0.291041	-0.014547	-0.205680	0.035857	0.841712	-0.209467	0.00000	15203.86117	15203.86117	83341.463418	...	100000.0	84661.585369	84661.585369	6	[]	0.0224	40276.571970	0.0224	-0.014547	0.035857
2015-03-07 23:59:00+00:00	0.277698	-0.003541	0.233649	0.035857	0.769546	-0.214621	0.00000	15203.86117	15203.86117	84442.073174	...	100000.0	83341.463418	83341.463418	7	[]	0.0224	22856.945604	0.0224	-0.003541	0.035857
2015-03-08 23:59:00+00:00	0.258004	-0.007748	0.068857	0.035857	0.713274	-0.212688	0.00000	15203.86117	15203.86117	84021.341464	...	100000.0	84442.073174	84442.073174	8	[]	0.0224	13853.794890	0.0224	-0.007748	0.035857
2015-03-09 23:59:00+00:00	0.351151	0.039234	1.427229	0.051793	0.669335	-0.180976	0.00000	15203.86117	15203.86117	88719.512199	...	100000.0	84021.341464	84021.341464	9	[]	0.0220	62908.377680	0.0220	0.039234	0.051793
2015-03-10 23:59:00+00:00	0.331070	0.044173	1.598363	0.159363	0.793556	-0.113480	0.00000	15203.86117	15203.86117	89213.414638	...	100000.0	88719.512199	88719.512199	10	[]	0.0214	67389.782195	0.0214	0.044173	0.159363
2015-03-11 23:59:00+00:00	0.315912	0.056307	1.581998	0.099602	0.819338	-0.112162	0.00000	15203.86117	15203.86117	90426.829272	...	100000.0	89213.414638	89213.414638	11	[]	0.0211	25811.471790	0.0211	0.056307	0.099602
2015-03-12 23:59:00+00:00	0.303546	0.053106	1.379340	0.099602	0.782516	-0.109012	0.00000	15203.86117	15203.86117	90106.707321	...	100000.0	90426.829272	90426.829272	12	[]	0.0210	27275.117559	0.0210	0.053106	0.099602
2015-03-13 23:59:00+00:00	0.340503	0.015392	0.536384	0.096056	0.751148	-0.091457	0.00000	15203.86117	15203.86117	86335.365857	...	100000.0	90106.707321	90106.707321	13	[]	0.0213	46670.674822	0.0213	0.015392	0.096056
2015-03-14 23:59:00+00:00	0.327414	0.013624	0.464604	0.096016	0.722505	-0.090370	0.00000	15203.86117	15203.86117	86158.536589	...	100000.0	86335.365857	86335.365857	14	[]	0.0213	23011.197461	0.0213	0.013624	0.096016
2015-03-15 23:59:00+00:00	0.319171	0.026734	0.671484	0.123936	0.700101	-0.082130	0.00000	15203.86117	15203.86117	87469.512195	...	100000.0	86158.536589	86158.536589	15	[]	0.0213	13427.567949	0.0213	0.026734	0.123936
2015-03-16 23:59:00+00:00	0.311356	0.039904	0.814079	0.095618	0.689624	-0.091220	0.00000	15203.86117	15203.86117	88786.585369	...	100000.0	87469.512195	87469.512195	16	[]	0.0210	22313.424619	0.0210	0.039904	0.095618
2015-03-17 23:59:00+00:00	0.311274	0.021703	0.499630	0.095618	0.668209	-0.086678	0.00000	15203.86117	15203.86117	86966.463418	...	100000.0	88786.585369	88786.585369	17	[]	0.0206	22235.369855	0.0206	0.021703	0.095618
2015-03-18 23:59:00+00:00	0.438883	-0.063785	-0.875710	0.044183	0.678082	0.058541	0.00000	15203.86117	15203.86117	78417.682927	...	100000.0	86966.463418	86966.463418	18	[]	0.0193	89923.415728	0.0193	-0.063785	0.044183
2015-03-19 23:59:00+00:00	0.431989	-0.049242	-0.635496	0.055165	0.659508	0.062612	0.00000	15203.86117	15203.86117	79871.951223	...	100000.0	78417.682927	78417.682927	19	[]	0.0198	78451.537176	0.0198	-0.049242	0.055165
2015-03-20 23:59:00+00:00	0.420599	-0.048602	-0.590673	0.050043	0.642625	0.061705	0.00000	15203.86117	15203.86117	79935.975613	...	100000.0	79871.951223	79871.951223	20	[]	0.0193	16197.477968	0.0193	-0.048602	0.050043
2015-03-21 23:59:00+00:00	0.410115	-0.053846	-0.627981	0.048578	0.626557	0.062140	0.00000	15203.86117	15203.86117	79411.585369	...	100000.0	79935.975613	79935.975613	21	[]	0.0193	15082.649219	0.0193	-0.053846	0.048578
2015-03-22 23:59:00+00:00	0.412261	-0.028388	-0.313487	0.065804	0.613145	0.073754	0.00000	15203.86117	15203.86117	81957.317076	...	100000.0	79411.585369	79411.585369	22	[]	0.0193	18263.490755	0.0193	-0.028388	0.065804
2015-03-23 23:59:00+00:00	0.403002	-0.033236	-0.334801	0.038540	0.606788	0.075368	0.00000	15203.86117	15203.86117	81472.560979	...	100000.0	81957.317076	81957.317076	23	[]	0.0192	23532.384930	0.0192	-0.033236	0.038540
2015-03-24 23:59:00+00:00	0.448116	-0.097961	-0.950042	-0.033763	0.637613	0.187571	0.00000	15203.86117	15203.86117	75000.000003	...	100000.0	81472.560979	81472.560979	24	[]	0.0188	64117.869871	0.0188	-0.097961	-0.033763
2015-03-25 23:59:00+00:00	0.438954	-0.097077	-0.940773	-0.014042	0.627763	0.188068	0.00000	15203.86117	15203.86117	75088.414637	...	100000.0	75000.000003	75000.000003	25	[]	0.0193	53254.531782	0.0193	-0.097077	-0.014042
2015-03-26 23:59:00+00:00	0.432277	-0.087809	-0.770002	-0.033763	0.618281	0.178979	0.00000	15203.86117	15203.86117	76015.243905	...	100000.0	75088.414637	75088.414637	26	[]	0.0201	33260.968413	0.0201	-0.087809	-0.033763
2015-03-27 23:59:00+00:00	0.424318	-0.096437	-0.900059	0.009373	0.621827	0.163263	0.00000	15203.86117	15203.86117	75152.439027	...	100000.0	76015.243905	76015.243905	27	[]	0.0195	22731.111936	0.0195	-0.096437	0.009373
2015-03-28 23:59:00+00:00	0.423172	-0.076772	-0.643934	-0.009050	0.612953	0.150150	0.00000	15203.86117	15203.86117	77118.902442	...	100000.0	75152.439027	75152.439027	28	[]	0.0195	17724.687732	0.0195	-0.076772	-0.009050
2015-03-29 23:59:00+00:00	0.425615	-0.107900	-0.910906	-0.012074	0.601994	0.152684	0.00000	15203.86117	15203.86117	74006.097564	...	100000.0	77118.902442	77118.902442	29	[]	0.0195	31825.768349	0.0195	-0.107900	-0.012074
2015-03-30 23:59:00+00:00	0.422749	-0.092077	-0.767531	0.013358	0.596112	0.163173	0.00000	15203.86117	15203.86117	75588.414637	...	100000.0	74006.097564	74006.097564	30	[]	0.0196	30725.359920	0.0196	-0.092077	0.013358
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017-05-30 23:59:00+00:00	0.427200	5.696856	0.157089	7.525896	0.553873	0.636433	0.00000	15203.86117	15203.86117	654481.707339	...	100000.0	672987.804904	672987.804904	822	[]	0.0221	21448.633495	0.0221	5.696856	7.525896
2017-05-31 23:59:00+00:00	0.427054	5.834356	0.157937	7.752988	0.553688	0.636502	0.00000	15203.86117	15203.86117	668231.707343	...	100000.0	654481.707339	654481.707339	823	[]	0.0221	21235.805383	0.0221	5.834356	7.752988
2017-06-01 23:59:00+00:00	0.427722	6.200819	0.162931	8.221495	0.554049	0.637450	0.00000	15203.86117	15203.86117	704878.048807	...	100000.0	668231.707343	668231.707343	824	[]	0.0221	18959.512934	0.0221	6.200819	8.221495
2017-06-02 23:59:00+00:00	0.427951	6.487100	0.166601	8.593625	0.554091	0.637943	0.00000	15203.86117	15203.86117	733506.097589	...	100000.0	704878.048807	704878.048807	825	[]	0.0215	12549.643232	0.0215	6.487100	8.593625
2017-06-03 23:59:00+00:00	0.427829	6.655087	0.169294	8.782075	0.553828	0.638093	0.00000	15203.86117	15203.86117	750304.878077	...	100000.0	733506.097589	733506.097589	826	[]	0.0215	10767.981906	0.0215	6.655087	8.782075
2017-06-04 23:59:00+00:00	0.427593	6.738014	0.170134	8.894622	0.553511	0.638115	0.00000	15203.86117	15203.86117	758597.561001	...	100000.0	750304.878077	750304.878077	827	[]	0.0215	11880.383420	0.0215	6.738014	8.894622
2017-06-05 23:59:00+00:00	0.428439	7.191368	0.174943	9.499428	0.554092	0.639197	0.00000	15203.86117	15203.86117	803932.926860	...	100000.0	758597.561001	758597.561001	828	[]	0.0218	15390.903185	0.0218	7.191368	9.499428
2017-06-06 23:59:00+00:00	0.430141	7.824600	0.180975	10.338645	0.555360	0.641121	0.00000	15203.86117	15203.86117	867256.097594	...	100000.0	803932.926860	803932.926860	829	[]	0.0214	42687.668790	0.0214	7.824600	10.338645
2017-06-07 23:59:00+00:00	0.431723	7.213014	0.171850	9.539858	0.556521	0.642924	0.00000	15203.86117	15203.86117	806097.561006	...	100000.0	867256.097594	867256.097594	830	[]	0.0218	22094.398527	0.0218	7.213014	9.539858
2017-06-08 23:59:00+00:00	0.432277	7.632222	0.175931	10.107570	0.556877	0.643699	0.00000	15203.86117	15203.86117	848018.292715	...	100000.0	806097.561006	806097.561006	831	[]	0.0219	16977.816089	0.0219	7.632222	10.107570
2017-06-09 23:59:00+00:00	0.432032	7.716063	0.176681	10.219521	0.556554	0.643715	0.00000	15203.86117	15203.86117	856402.439057	...	100000.0	848018.292715	848018.292715	832	[]	0.0221	9521.116767	0.0221	7.716063	10.219521
2017-06-10 23:59:00+00:00	0.431778	7.706917	0.176444	10.202471	0.556226	0.643719	0.00000	15203.86117	15203.86117	855487.804911	...	100000.0	856402.439057	856402.439057	833	[]	0.0221	16167.315725	0.0221	7.706917	10.202471
2017-06-11 23:59:00+00:00	0.432214	8.120941	0.181278	10.701594	0.556349	0.644447	0.00000	15203.86117	15203.86117	896890.243937	...	100000.0	855487.804911	855487.804911	834	[]	0.0221	19034.136815	0.0221	8.120941	10.701594
2017-06-12 23:59:00+00:00	0.437592	6.986185	0.162983	9.243228	0.560459	0.649967	0.00000	15203.86117	15203.86117	783414.634176	...	100000.0	896890.243937	896890.243937	835	[]	0.0221	48340.947688	0.0221	6.986185	9.243228
2017-06-13 23:59:00+00:00	0.437831	7.313929	0.166669	9.664441	0.560505	0.650461	0.00000	15203.86117	15203.86117	816189.024421	...	100000.0	783414.634176	783414.634176	836	[]	0.0221	23172.058884	0.0221	7.313929	9.664441
2017-06-14 23:59:00+00:00	0.441463	6.451734	0.153438	8.509086	0.563520	0.654055	0.00000	15203.86117	15203.86117	729969.512223	...	100000.0	816189.024421	816189.024421	837	[]	0.0215	34717.773840	0.0215	6.451734	8.509086
2017-06-15 23:59:00+00:00	0.441231	6.400514	0.152175	8.460040	0.563202	0.654088	0.00000	15203.86117	15203.86117	724847.561003	...	100000.0	729969.512223	729969.512223	838	[]	0.0216	55839.127994	0.0216	6.400514	8.460040
2017-06-16 23:59:00+00:00	0.441132	6.583441	0.154278	8.701195	0.562997	0.654244	0.00000	15203.86117	15203.86117	743140.243931	...	100000.0	724847.561003	724847.561003	839	[]	0.0216	22402.387929	0.0216	6.583441	8.701195
2017-06-17 23:59:00+00:00	0.442375	7.109661	0.159795	9.387043	0.563869	0.655675	0.00000	15203.86117	15203.86117	795762.195152	...	100000.0	743140.243931	743140.243931	840	[]	0.0216	16637.617543	0.0216	7.109661	9.387043
2017-06-18 23:59:00+00:00	0.442876	6.747770	0.154265	8.929482	0.564133	0.656408	0.00000	15203.86117	15203.86117	759573.170761	...	100000.0	795762.195152	795762.195152	841	[]	0.0216	19988.156662	0.0216	6.747770	8.929482
2017-06-19 23:59:00+00:00	0.442976	7.023990	0.157300	9.291024	0.564087	0.656754	0.00000	15203.86117	15203.86117	787195.121981	...	100000.0	759573.170761	759573.170761	842	[]	0.0219	13719.302504	0.0219	7.023990	9.291024
2017-06-20 23:59:00+00:00	0.443473	7.427953	0.165028	9.629482	0.563982	0.657532	0.00000	15203.86117	15203.86117	827591.463446	...	100000.0	787195.121981	787195.121981	843	[]	0.0216	20548.879169	0.0216	7.427953	9.629482
2017-06-21 23:59:00+00:00	0.443635	7.153258	0.158260	9.438247	0.563769	0.657964	0.00000	15203.86117	15203.86117	800121.951250	...	100000.0	827591.463446	827591.463446	844	[]	0.0216	24534.642260	0.0216	7.153258	9.438247
2017-06-22 23:59:00+00:00	0.443450	7.300819	0.159417	9.650902	0.563511	0.658029	0.00000	15203.86117	15203.86117	814878.048812	...	100000.0	800121.951250	800121.951250	845	[]	0.0215	15181.549108	0.0215	7.300819	9.650902
2017-06-23 23:59:00+00:00	0.443189	7.307222	0.159709	9.637450	0.563183	0.658024	0.00000	15203.86117	15203.86117	815518.292714	...	100000.0	814878.048812	814878.048812	846	[]	0.0215	8139.412746	0.0215	7.307222	9.637450
2017-06-24 23:59:00+00:00	0.444394	6.781917	0.151725	8.974032	0.563994	0.659430	0.00000	15203.86117	15203.86117	762987.804907	...	100000.0	815518.292714	815518.292714	847	[]	0.0215	16770.845452	0.0215	6.781917	8.974032
2017-06-25 23:59:00+00:00	0.444167	6.723075	0.150972	8.887696	0.563699	0.659457	0.00000	15203.86117	15203.86117	757103.658565	...	100000.0	762987.804907	762987.804907	848	[]	0.0215	18137.882004	0.0215	6.723075	8.887696
2017-06-26 23:59:00+00:00	0.444393	6.449600	0.146720	8.542984	0.563751	0.659922	0.00000	15203.86117	15203.86117	729756.097589	...	100000.0	757103.658565	757103.658565	849	[]	0.0214	36367.695758	0.0214	6.449600	8.542984
2017-06-27 23:59:00+00:00	0.444947	6.838624	0.151955	9.000000	0.563942	0.660755	0.00000	15203.86117	15203.86117	768658.536612	...	100000.0	729756.097589	729756.097589	850	[]	0.0221	33486.182337	0.0221	6.838624	9.000000
2017-06-28 23:59:00+00:00	0.444691	6.829478	0.151427	9.000000	0.563613	0.660760	0.00000	15203.86117	15203.86117	767743.902465	...	100000.0	768658.536612	768658.536612	851	[]	0.0221	21827.313647	0.0221	6.829478	9.000000

851 rows × 45 columns

Also, instead of defining an output file we are accessing it via the "_" variable that will be created in the name space and contain the performance DataFrame.

In [4]:

_.head()

Out [4]:

	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	cash	ending_cash	ending_exposure	...	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return	volume	treasury	algorithm	benchmark
2015-03-01 23:59:00+00:00	NaN	0.000000	NaN	0.045833	NaN	NaN	0.000000	100000.000000	100000.000000	0.000	...	100000.0	0.000	0.000	1	[]	0.0200	317	0.0200	0.000000	0.045833
2015-03-02 23:59:00+00:00	0.000278	-0.000025	0.011045	0.120833	0.290503	-0.000956	-85544.474955	14455.525045	14455.525045	85542.000	...	100000.0	0.000	0.000	2	[{u'commission': None, u'amount': 318, u'sid':...	0.0208	98063	0.0208	-0.000025	0.120833
2015-03-03 23:59:00+00:00	0.051796	-0.005688	-1.197544	0.113416	0.633538	0.077239	0.000000	14455.525045	14455.525045	84975.642	...	100000.0	85542.000	85542.000	3	[]	0.0212	442983	0.0212	-0.005688	0.113416
2015-03-04 23:59:00+00:00	0.342118	0.034955	0.401861	0.166666	0.524400	0.181468	0.000000	14455.525045	14455.525045	89040.000	...	100000.0	84975.642	84975.642	4	[]	0.0212	245889	0.0212	0.034955	0.166666
2015-03-05 23:59:00+00:00	0.637226	-0.038185	-3.914003	0.070834	0.976896	0.550520	0.000000	14455.525045	14455.525045	81726.000	...	100000.0	89040.000	89040.000	5	[]	0.0211	117440	0.0211	-0.038185	0.070834

5 rows × 45 columns

224 KiB Raw Blame History Unescape Escape

Running Catalyst in Jupyter Notebook

224 KiB

Raw Blame History