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Commit 5b940f54 authored by Dr.李's avatar Dr.李
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update examples

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notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ 5b940f54
......@@ -40,7 +40,7 @@
"ref_date = '2018-02-08'\n",
"\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['ashare_ex'])\n",
"universe = Universe('ashare_ex')\n",
"codes = engine.fetch_codes(ref_date, universe)\n",
"\n",
"risk_cov, risk_exposure = engine.fetch_risk_model(ref_date, codes)\n",
......
notebooks/Example 11 - Long Short Strategy Model.ipynb
View file @ 5b940f54
......@@ -38,7 +38,7 @@
"freq = '5b'\n",
"horizon = map_freq(freq)\n",
"neutralized_risk = risk_styles + industry_styles\n",
"universe = Universe(\"custom\", ['zz500'])\n",
"universe = Universe('zz500')\n",
"data_source = os.environ['DB_URI']\n",
"offset = 1\n",
"method = 'ls'\n",
......@@ -185,7 +185,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.5"
},
"varInspector": {
"cols": {
......
notebooks/Example 13 - Evaluation within Industry Groups.ipynb
View file @ 5b940f54
......@@ -30,7 +30,7 @@
"from alphamind.api import *\n",
"\n",
"factor = 'CFO2EV'\n",
"universe = Universe('custom', ['zz800'])\n",
"universe = Universe('zz800')\n",
"start_date = '2010-01-01'\n",
"end_date = '2018-04-26'\n",
"freq = '20b'\n",
......@@ -185,7 +185,7 @@
"metadata": {},
"outputs": [],
"source": [
"sample_factor3 = sample_factor3[['code', 'isOpen', 'f3', 'industry']]\n",
"sample_factor3 = sample_factor3[['code', 'f3', 'industry']]\n",
"sample_factor3.sort_values('f3', ascending=False).head(15)"
]
},
......@@ -306,7 +306,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.5"
},
"varInspector": {
"cols": {
......
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ 5b940f54
......@@ -116,12 +116,7 @@
"outputs": [],
"source": [
"# Running settings\n",
"running_setting = RunningSetting(universe,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
"running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" rebalance_method=method,\n",
" bounds=bounds,\n",
" turn_over_target=turn_over_target)"
......@@ -134,8 +129,16 @@
"outputs": [],
"source": [
"# Strategy\n",
"strategy = Strategy(alpha_model, data_meta, running_setting)\n",
"ret_df, positions = strategy.run()"
"strategy = Strategy(alpha_model,\n",
" data_meta,\n",
" universe=universe,\n",
" start_date=start_date,\n",
" end_date=end_date,\n",
" freq=freq,\n",
" benchmark=benchmark_code)\n",
"\n",
"strategy.prepare_backtest_data()\n",
"ret_df, positions = strategy.run(running_setting=running_setting)"
]
},
{
......
notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ 5b940f54
......@@ -42,32 +42,42 @@
"benchmark_code = 905\n",
"universe = Universe('zz800') + Universe('cyb')\n",
"\n",
"def create_scenario(weights_bandwidth=0.02, target_vol=0.01, method='risk_neutral'):\n",
" start_date = '2017-01-01'\n",
" end_date = '2018-04-27'\n",
" freq = '10b'\n",
" neutralized_risk = None\n",
"\n",
" alpha_factors = {\n",
" 'f01': CSQuantiles(LAST('EPS'), groups='sw1'),\n",
" 'f02': CSQuantiles(LAST('ROE'), groups='sw1'),\n",
" }\n",
"\n",
" weights = dict(\n",
" f01=1.,\n",
" f02=1.\n",
" )\n",
"\n",
" alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
"\n",
" data_meta = DataMeta(freq=freq,\n",
" universe=universe,\n",
" batch=1,\n",
" neutralized_risk=None,\n",
" pre_process=None,\n",
" post_process=None,\n",
" data_source=os.environ['DB_URI'])\n",
"start_date = '2017-01-01'\n",
"end_date = '2018-04-27'\n",
"freq = '10b'\n",
"neutralized_risk = None\n",
"\n",
"alpha_factors = {\n",
" 'f01': CSQuantiles(LAST('EPS'), groups='sw1'),\n",
" 'f02': CSQuantiles(LAST('ROE'), groups='sw1'),\n",
"}\n",
"\n",
"weights = dict(\n",
" f01=1.,\n",
" f02=1.\n",
" )\n",
"\n",
"alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
"\n",
"data_meta = DataMeta(freq=freq,\n",
" universe=universe,\n",
" batch=1,\n",
" neutralized_risk=None,\n",
" pre_process=None,\n",
" post_process=None,\n",
" data_source=os.environ['DB_URI'])\n",
"\n",
"strategy = Strategy(alpha_model,\n",
" data_meta,\n",
" universe=universe,\n",
" start_date=start_date,\n",
" end_date=end_date,\n",
" freq=freq,\n",
" benchmark=benchmark_code)\n",
"\n",
"strategy.prepare_backtest_data()\n",
"\n",
"def create_scenario(weights_bandwidth=0.02, target_vol=0.01, method='risk_neutral'):\n",
" industry_names = industry_list('sw', 1)\n",
" constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
" total_risk_names = constraint_risk + ['benchmark', 'total']\n",
......@@ -93,19 +103,13 @@
" u_val.append(0.0)\n",
"\n",
" bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)\n",
" running_setting = RunningSetting(universe,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
" running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" rebalance_method=method,\n",
" bounds=bounds,\n",
" target_vol=target_vol,\n",
" turn_over_target=0.4)\n",
"\n",
" strategy = Strategy(alpha_model, data_meta, running_setting)\n",
" ret_df, positions = strategy.run()\n",
" ret_df, positions = strategy.run(running_setting)\n",
" return ret_df"
]
},
......@@ -150,6 +154,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
"\n",
"with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_gap.xlsx', engine='xlsxwriter') as writer:\n",
......@@ -166,6 +171,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"target_vols = [0.015, 0.030, 0.045, 0.060]\n",
"\n",
"with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_tv.xlsx', engine='xlsxwriter') as writer:\n",
......@@ -190,6 +196,18 @@
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"varInspector": {
"cols": {
"lenName": 16,
......
notebooks/Example 6 - Target Volatility Builder.ipynb
View file @ 5b940f54
......@@ -224,12 +224,7 @@
"\"\"\"\n",
"Running Settings\n",
"\"\"\"\n",
"running_setting = RunningSetting(universe,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
"running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" rebalance_method='tv',\n",
" bounds=bounds,\n",
" target_vol=target_vol)"
......@@ -244,8 +239,15 @@
"\"\"\"\n",
"Strategy run\n",
"\"\"\"\n",
"strategy = Strategy(alpha_model, data_meta, running_setting)\n",
"ret_df, positions = strategy.run()"
"strategy = Strategy(alpha_model,\n",
" data_meta,\n",
" universe=universe,\n",
" start_date=start_date,\n",
" end_date=end_date,\n",
" freq=freq,\n",
" benchmark=benchmark_code)\n",
"strategy.prepare_backtest_data()\n",
"ret_df, positions = strategy.run(running_setting)"
]
},
{
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ 5b940f54
......@@ -63,7 +63,7 @@
"data_source = os.environ['DB_URI']\n",
"engine = SqlEngine(data_source)\n",
"\n",
"universes = [Universe('custom', [u_name]) for u_name in u_names]\n",
"universes = [Universe(u_name) for u_name in u_names]\n",
"codes_set = [engine.fetch_codes(ref_date, universe=universe) for universe in universes]\n",
"data_set = [engine.fetch_data(ref_date, factor, codes, benchmark=b_code, risk_model=risk_model) for codes, b_code in zip(codes_set, b_codes)]"
]
......
notebooks/Example 8 - Neutralize Algorithm Comparison.ipynb
View file @ 5b940f54
......@@ -46,7 +46,7 @@
"factor = 'EPS'\n",
"\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['zz800'])"
"universe = Universe('zz800')"
]
},
{
......
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