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release-0.5.0 (#1)
* init commit * change the version number * rich the docs&fix cache docs * update index readme * Modify cache class name * Modify sharpe to information_ratio * Modify Group- to Group * add the description of graphical results & fix the backtest docs * fix docs in details * update docs * Update introduction.rst * Update README.md * Update introduction.rst * Update introduction.rst * Update introduction.rst * Update installation.rst * Update installation.rst * Update initialization.rst * Update getdata.rst * Update integration.rst * Update initialization.rst * Update getdata.rst * Update estimator.rst Modify some typos. * Update README.md Modify the typos. * Update initialization.rst * Update data.rst * Update report.rst * Update estimator.rst * Update cumulative_return.py * Update model.rst * Update rank_label.py * Update cumulative_return.py * Update strategy.rst * Update getdata.rst * Update backtest.rst * Update integration.rst * Update getdata.rst * Update introduction.rst * Update introduction.rst * Update README.md * Update report.rst * Update integration.rst Fix typos * Update installation.rst Fix typos * Update getdata.rst * Update initialization.rst Fix typos. * add quick start docs&fix detials * fix estimator docs & fix strategy docs * fix the cahce in data.rst * update documents * Fix Corr && Rsquare * fix data retrival example to csi300 & fix a data bug * fix filter bug * Fix data collector * Modift model args * add the log & fix README.md\quick.rst * add enviroment depend & add intoduction of qlib-server online mode * fix image center fomat & set log_only of docs is True * fix README.md format * update data preparation & readme logo image * get_data support version * Modify analysis names * Modify analysis graph * update report.rst & data.rst * commmit estimator for merge * minimal requirements * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update READEME.md * Update READEME.md * update estimator * Fix doc urls * fix get_data.py docstring * update test_get_data.py * Upate docs * Upate docs * Upate docs Co-authored-by: bxdd <bxddream@gmail.com> Co-authored-by: zhupr <zhu.pengrong@foxmail.com> Co-authored-by: Wendi Li <wendili.academic@qq.com> Co-authored-by: Dingsu Wang <dingsu.wang@gmail.com> Co-authored-by: bxdd <45119470+bxdd@users.noreply.github.com> Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com>
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you-n-g
@@ -7,7 +7,7 @@ Intraday Trading: Model&Strategy Testing
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Introduction
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===================
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``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of custom model/strategy.
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``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of a custom model/strategy.
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.. note::
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@@ -19,11 +19,11 @@ Introduction
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Example
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===========================
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Users need to generate a prediction score(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
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Users need to generate a `prediction score`(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
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a `TopkDropoutStrategy` strategy with `(topk=50, n_drop=5, risk_degree=0.95, limit_threshold=0.0095)` will be used.
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If ``Strategy`` module is not user's interested part, `TopkDropoutStrategy` is enough.
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If ``Strategy`` module is not users' interested part, `TopkDropoutStrategy` is enough.
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The simple example with default strategy is as follows.
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The simple example of the default strategy is as follows.
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.. code-block:: python
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@@ -31,14 +31,14 @@ The simple example with default strategy is as follows.
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# pred_score is the prediction score
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report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
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To know more about backtesting with specific strategy, please refer to `Strategy <strategy.html>`_.
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To know more about backtesting with a specific strategy, please refer to `Strategy <strategy.html>`_.
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To know more about the prediction score `pred_score` output by ``Model``, please refer to `Interday Model: Model Training & Prediction <model.html>`_.
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Prediction Score
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-----------------
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The prediction score is a pandas DataFrame. Its index is <instrument(str), datetime(pd.Timestamp)> and it must
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The `prediction score` is a pandas DataFrame. Its index is <instrument(str), datetime(pd.Timestamp)> and it must
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contains a `score` column.
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A prediction sample is shown as follows.
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@@ -67,37 +67,44 @@ The backtest results are in the following form:
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.. code-block:: python
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sub_bench mean 0.000662
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std 0.004487
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annual 0.166720
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sharpe 2.340526
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mdd -0.080516
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sub_cost mean 0.000577
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std 0.004482
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annual 0.145392
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sharpe 2.043494
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mdd -0.083584
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risk
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excess_return_without_cost mean 0.000605
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std 0.005481
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annualized_return 0.152373
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information_ratio 1.751319
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max_drawdown -0.059055
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excess_return_with_cost mean 0.000410
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std 0.005478
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annualized_return 0.103265
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information_ratio 1.187411
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max_drawdown -0.075024
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- `sub_bench`
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Returns of the portfolio without deduction of fees
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- `sub_cost`
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Returns of the portfolio with deduction of fees
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- `mean`
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Mean value of the returns sequence(difference sequence of assets).
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- `excess_return_without_cost`
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- `mean`
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Mean value of the `CAR` (cumulative abnormal return) without cost
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- `std`
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The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
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- `annualized_return`
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The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
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- `information_ratio`
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The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
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- `max_drawdown`
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The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
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- `std`
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Standard deviation of the returns sequence(difference sequence of assets).
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- `excess_return_with_cost`
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- `mean`
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Mean value of the `CAR` (cumulative abnormal return) series with cost
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- `std`
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The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
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- `annualized_return`
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The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
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- `information_ratio`
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The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
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- `max_drawdown`
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The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
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- `annual`
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Average annualized returns of the portfolio.
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- `ir`
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Information Ratio, please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
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- `mdd`
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Maximum Drawdown, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
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Reference
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