init commit

qlib/contrib/evaluate_portfolio.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import numpy as np
+import pandas as pd
+from scipy.stats import spearmanr, pearsonr
+
+
+from ..data import D
+
+from collections import OrderedDict
+
+
+def _get_position_value_from_df(evaluate_date, position, close_data_df):
+    """Get position value by existed close data df
+    close_data_df:
+        pd.DataFrame
+        multi-index
+        close_data_df['$close'][stock_id][evaluate_date]: close price for (stock_id, evaluate_date)
+    position:
+        same in get_position_value()
+    """
+    value = 0
+    for stock_id, report in position.items():
+        if stock_id != "cash":
+            value += report["amount"] * close_data_df["$close"][stock_id][evaluate_date]
+            # value += report['amount'] * report['price']
+    if "cash" in position:
+        value += position["cash"]
+    return value
+
+
+def get_position_value(evaluate_date, position):
+    """sum of close*amount
+
+    get value of postion
+
+    use close price
+
+        postions:
+        {
+            Timestamp('2016-01-05 00:00:00'):
+            {
+                'SH600022':
+                {
+                    'amount':100.00,
+                    'price':12.00
+                },
+
+                'cash':100000.0
+            }
+        }
+
+    It means Hold 100.0 'SH600022' and 100000.0 RMB in '2016-01-05'
+    """
+    # load close price for position
+    # position should also consider cash
+    instruments = list(position.keys())
+    instruments = list(set(instruments) - set(["cash"]))  # filter 'cash'
+    fields = ["$close"]
+    close_data_df = D.features(
+        instruments,
+        fields,
+        start_time=evaluate_date,
+        end_time=evaluate_date,
+        freq="day",
+        disk_cache=0,
+    )
+    value = _get_position_value_from_df(evaluate_date, position, close_data_df)
+    return value
+
+
+def get_position_list_value(positions):
+    # generate instrument list and date for whole poitions
+    instruments = set()
+    for day, position in positions.items():
+        instruments.update(position.keys())
+    instruments = list(set(instruments) - set(["cash"]))  # filter 'cash'
+    instruments.sort()
+    day_list = list(positions.keys())
+    day_list.sort()
+    start_date, end_date = day_list[0], day_list[-1]
+    # load data
+    fields = ["$close"]
+    close_data_df = D.features(
+        instruments,
+        fields,
+        start_time=start_date,
+        end_time=end_date,
+        freq="day",
+        disk_cache=0,
+    )
+    # generate value
+    # return dict for time:position_value
+    value_dict = OrderedDict()
+    for day, position in positions.items():
+        value = _get_position_value_from_df(evaluate_date=day, position=position, close_data_df=close_data_df)
+        value_dict[day] = value
+    return value_dict
+
+
+def get_daily_return_series_from_positions(positions, init_asset_value):
+    """Parameters
+    generate daily return series from  position view
+    positions: positions generated by strategy
+    init_asset_value : init asset value
+    return: pd.Series of daily return , return_series[date] = daily return rate
+    """
+    value_dict = get_position_list_value(positions)
+    value_series = pd.Series(value_dict)
+    value_series = value_series.sort_index()  # check date
+    return_series = value_series.pct_change()
+    return_series[value_series.index[0]] = (
+        value_series[value_series.index[0]] / init_asset_value - 1
+    )  # update daily return for the first date
+    return return_series
+
+
+def get_annual_return_from_positions(positions, init_asset_value):
+    """Annualized Returns
+
+    p_r = (p_end / p_start)^{(250/n)} - 1
+
+    p_r     annual return
+    p_end   final value
+    p_start init value
+    n       days of backtest
+
+    """
+    date_range_list = sorted(list(positions.keys()))
+    end_time = date_range_list[-1]
+    p_end = get_position_value(end_time, positions[end_time])
+    p_start = init_asset_value
+    n_period = len(date_range_list)
+    annual = pow((p_end / p_start), (250 / n_period)) - 1
+
+    return annual
+
+
+def get_annaul_return_from_return_series(r, method="ci"):
+    """Risk Analysis from daily return series
+
+    Parameters
+    ----------
+    r : pandas.Series
+        daily return series
+    method : str
+        interest calculation method, ci(compound interest)/si(simple interest)
+    """
+    mean = r.mean()
+    annual = (1 + mean) ** 250 - 1 if method == "ci" else mean * 250
+
+    return annual
+
+
+def get_sharpe_ratio_from_return_series(r, risk_free_rate=0.00, method="ci"):
+    """Risk Analysis
+
+    Parameters
+    ----------
+    r : pandas.Series
+        daily return series
+    method : str
+        interest calculation method, ci(compound interest)/si(simple interest)
+    risk_free_rate : float
+        risk_free_rate, default as 0.00, can set as 0.03 etc
+    """
+    std = r.std(ddof=1)
+    annual = get_annaul_return_from_return_series(r, method=method)
+    sharpe = (annual - risk_free_rate) / std / np.sqrt(250)
+
+    return sharpe
+
+
+def get_max_drawdown_from_series(r):
+    """Risk Analysis from asset value
+
+    cumprod way
+
+    Parameters
+    ----------
+    r : pandas.Series
+        daily return series
+    """
+    # mdd = ((r.cumsum() - r.cumsum().cummax()) / (1 + r.cumsum().cummax())).min()
+
+    mdd = (((1 + r).cumprod() - (1 + r).cumprod().cummax()) / ((1 + r).cumprod().cummax())).min()
+
+    return mdd
+
+
+def get_turnover_rate():
+    # in backtest
+    pass
+
+
+def get_beta(r, b):
+    """Risk Analysis  beta
+
+    Parameters
+    ----------
+    r : pandas.Series
+        daily return series of strategy
+    b : pandas.Series
+        daily return series of baseline
+    """
+    cov_r_b = np.cov(r, b)
+    var_b = np.var(b)
+    return cov_r_b / var_b
+
+
+def get_alpha(r, b, risk_free_rate=0.03):
+    beta = get_beta(r, b)
+    annaul_r = get_annaul_return_from_return_series(r)
+    annaul_b = get_annaul_return_from_return_series(b)
+
+    alpha = annaul_r - risk_free_rate - beta * (annaul_b - risk_free_rate)
+
+    return alpha
+
+
+def get_volatility_from_series(r):
+    return r.std(ddof=1)
+
+
+def get_rank_ic(a, b):
+    """Rank IC
+
+    Parameters
+    ----------
+    r : pandas.Series
+        daily score series of feature
+    b : pandas.Series
+        daily return series
+
+    """
+    return spearmanr(a, b).correlation
+
+
+def get_normal_ic(a, b):
+    return pearsonr(a, b).correlation