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Unverified Commit b3973859 authored by iLampard's avatar iLampard Committed by GitHub
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Merge pull request #12 from alpha-miner/master 

merge update
parents f4ddddae 5b940f54
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Showing with 610 additions and 403 deletions
alphamind/model/data_preparing.py
View file @ b3973859
...@@ -108,7 +108,7 @@ def prepare_data(engine: SqlEngine, ...@@ -108,7 +108,7 @@ def prepare_data(engine: SqlEngine,
df.dropna(inplace=True) df.dropna(inplace=True)
return dates, df[['trade_date', 'code', 'dx']], df[ return dates, df[['trade_date', 'code', 'dx']], df[
['trade_date', 'code', 'weight', 'isOpen', 'industry_code', 'industry'] + transformer.names] ['trade_date', 'code', 'weight', 'industry_code', 'industry'] + transformer.names]
def batch_processing(names, def batch_processing(names,
...@@ -231,7 +231,6 @@ def fetch_data_package(engine: SqlEngine, ...@@ -231,7 +231,6 @@ def fetch_data_package(engine: SqlEngine,
target_df['weight'] = train_x['weight'] target_df['weight'] = train_x['weight']
target_df['industry'] = train_x['industry'] target_df['industry'] = train_x['industry']
target_df['industry_code'] = train_x['industry_code'] target_df['industry_code'] = train_x['industry_code']
target_df['isOpen'] = train_x['isOpen']
if neutralized_risk: if neutralized_risk:
for i, name in enumerate(neutralized_risk): for i, name in enumerate(neutralized_risk):
...@@ -314,7 +313,7 @@ def fetch_train_phase(engine, ...@@ -314,7 +313,7 @@ def fetch_train_phase(engine,
df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna() df = pd.merge(factor_df, target_df, on=['trade_date', 'code']).dropna()
target_df, factor_df = df[['trade_date', 'code', 'dx']], df[ target_df, factor_df = df[['trade_date', 'code', 'dx']], df[
['trade_date', 'code', 'isOpen'] + transformer.names] ['trade_date', 'code'] + transformer.names]
target_df, dates, date_label, risk_exp, x_values, y_values, _, _, codes = \ target_df, dates, date_label, risk_exp, x_values, y_values, _, _, codes = \
_merge_df(engine, transformer.names, factor_df, target_df, universe, dates, risk_model, neutralized_risk) _merge_df(engine, transformer.names, factor_df, target_df, universe, dates, risk_model, neutralized_risk)
......
alphamind/strategy/strategy.py
View file @ b3973859
...@@ -166,7 +166,7 @@ class Strategy(object): ...@@ -166,7 +166,7 @@ class Strategy(object):
if self.dask_client is None: if self.dask_client is None:
models = {} models = {}
for ref_date, _ in self.total_data_groups: for ref_date, _ in total_data_groups:
models[ref_date] = train_model(ref_date.strftime('%Y-%m-%d'), self.alpha_model, self.data_meta) models[ref_date] = train_model(ref_date.strftime('%Y-%m-%d'), self.alpha_model, self.data_meta)
else: else:
def worker(parameters): def worker(parameters):
...@@ -334,8 +334,8 @@ if __name__ == '__main__': ...@@ -334,8 +334,8 @@ if __name__ == '__main__':
universe=universe, universe=universe,
batch=1, batch=1,
neutralized_risk=neutralized_risk, neutralized_risk=neutralized_risk,
pre_process=None, # [winsorize_normal, standardize], pre_process=None,
post_process=None, # [standardize], post_process=None,
warm_start=1) warm_start=1)
industries = industry_list('sw_adj', 1) industries = industry_list('sw_adj', 1)
......
notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ b3973859
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
"ref_date = '2018-02-08'\n", "ref_date = '2018-02-08'\n",
"\n", "\n",
"engine = SqlEngine(os.environ['DB_URI'])\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", "codes = engine.fetch_codes(ref_date, universe)\n",
"\n", "\n",
"risk_cov, risk_exposure = engine.fetch_risk_model(ref_date, codes)\n", "risk_cov, risk_exposure = engine.fetch_risk_model(ref_date, codes)\n",
......
notebooks/Example 11 - Long Short Strategy Model.ipynb
View file @ b3973859
...@@ -38,7 +38,7 @@ ...@@ -38,7 +38,7 @@
"freq = '5b'\n", "freq = '5b'\n",
"horizon = map_freq(freq)\n", "horizon = map_freq(freq)\n",
"neutralized_risk = risk_styles + industry_styles\n", "neutralized_risk = risk_styles + industry_styles\n",
"universe = Universe(\"custom\", ['zz500'])\n", "universe = Universe('zz500')\n",
"data_source = os.environ['DB_URI']\n", "data_source = os.environ['DB_URI']\n",
"offset = 1\n", "offset = 1\n",
"method = 'ls'\n", "method = 'ls'\n",
...@@ -185,7 +185,7 @@ ...@@ -185,7 +185,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.5"
}, },
"varInspector": { "varInspector": {
"cols": { "cols": {
......
notebooks/Example 13 - Evaluation within Industry Groups.ipynb
View file @ b3973859
...@@ -30,7 +30,7 @@ ...@@ -30,7 +30,7 @@
"from alphamind.api import *\n", "from alphamind.api import *\n",
"\n", "\n",
"factor = 'CFO2EV'\n", "factor = 'CFO2EV'\n",
"universe = Universe('custom', ['zz800'])\n", "universe = Universe('zz800')\n",
"start_date = '2010-01-01'\n", "start_date = '2010-01-01'\n",
"end_date = '2018-04-26'\n", "end_date = '2018-04-26'\n",
"freq = '20b'\n", "freq = '20b'\n",
...@@ -185,7 +185,7 @@ ...@@ -185,7 +185,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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)" "sample_factor3.sort_values('f3', ascending=False).head(15)"
] ]
}, },
...@@ -306,7 +306,7 @@ ...@@ -306,7 +306,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.5"
}, },
"varInspector": { "varInspector": {
"cols": { "cols": {
......
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ b3973859
...@@ -116,12 +116,7 @@ ...@@ -116,12 +116,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# Running settings\n", "# Running settings\n",
"running_setting = RunningSetting(universe,\n", "running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
" rebalance_method=method,\n", " rebalance_method=method,\n",
" bounds=bounds,\n", " bounds=bounds,\n",
" turn_over_target=turn_over_target)" " turn_over_target=turn_over_target)"
...@@ -134,8 +129,16 @@ ...@@ -134,8 +129,16 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# Strategy\n", "# Strategy\n",
"strategy = Strategy(alpha_model, data_meta, running_setting)\n", "strategy = Strategy(alpha_model,\n",
"ret_df, positions = strategy.run()" " 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 @ b3973859
...@@ -42,32 +42,42 @@ ...@@ -42,32 +42,42 @@
"benchmark_code = 905\n", "benchmark_code = 905\n",
"universe = Universe('zz800') + Universe('cyb')\n", "universe = Universe('zz800') + Universe('cyb')\n",
"\n", "\n",
"def create_scenario(weights_bandwidth=0.02, target_vol=0.01, method='risk_neutral'):\n", "start_date = '2017-01-01'\n",
" start_date = '2017-01-01'\n", "end_date = '2018-04-27'\n",
" end_date = '2018-04-27'\n", "freq = '10b'\n",
" freq = '10b'\n", "neutralized_risk = None\n",
" neutralized_risk = None\n", "\n",
"\n", "alpha_factors = {\n",
" alpha_factors = {\n", " 'f01': CSQuantiles(LAST('EPS'), groups='sw1'),\n",
" 'f01': CSQuantiles(LAST('EPS'), groups='sw1'),\n", " 'f02': CSQuantiles(LAST('ROE'), groups='sw1'),\n",
" 'f02': CSQuantiles(LAST('ROE'), groups='sw1'),\n", "}\n",
" }\n", "\n",
"\n", "weights = dict(\n",
" weights = dict(\n", " f01=1.,\n",
" f01=1.,\n", " f02=1.\n",
" f02=1.\n", " )\n",
" )\n", "\n",
"\n", "alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
" alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n", "\n",
"\n", "data_meta = DataMeta(freq=freq,\n",
" data_meta = DataMeta(freq=freq,\n", " universe=universe,\n",
" universe=universe,\n", " batch=1,\n",
" batch=1,\n", " neutralized_risk=None,\n",
" neutralized_risk=None,\n", " pre_process=None,\n",
" pre_process=None,\n", " post_process=None,\n",
" post_process=None,\n", " data_source=os.environ['DB_URI'])\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", "\n",
"def create_scenario(weights_bandwidth=0.02, target_vol=0.01, method='risk_neutral'):\n",
" industry_names = industry_list('sw', 1)\n", " industry_names = industry_list('sw', 1)\n",
" constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n", " constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
" total_risk_names = constraint_risk + ['benchmark', 'total']\n", " total_risk_names = constraint_risk + ['benchmark', 'total']\n",
...@@ -93,19 +103,13 @@ ...@@ -93,19 +103,13 @@
" u_val.append(0.0)\n", " u_val.append(0.0)\n",
"\n", "\n",
" bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)\n", " bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)\n",
" running_setting = RunningSetting(universe,\n", " running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
" rebalance_method=method,\n", " rebalance_method=method,\n",
" bounds=bounds,\n", " bounds=bounds,\n",
" target_vol=target_vol,\n", " target_vol=target_vol,\n",
" turn_over_target=0.4)\n", " turn_over_target=0.4)\n",
"\n", "\n",
" strategy = Strategy(alpha_model, data_meta, running_setting)\n", " ret_df, positions = strategy.run(running_setting)\n",
" ret_df, positions = strategy.run()\n",
" return ret_df" " return ret_df"
] ]
}, },
...@@ -150,6 +154,7 @@ ...@@ -150,6 +154,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%time\n",
"weight_gaps = [0.005, 0.010, 0.015, 0.020]\n", "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
"\n", "\n",
"with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_gap.xlsx', engine='xlsxwriter') as writer:\n", "with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_gap.xlsx', engine='xlsxwriter') as writer:\n",
...@@ -166,6 +171,7 @@ ...@@ -166,6 +171,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%time\n",
"target_vols = [0.015, 0.030, 0.045, 0.060]\n", "target_vols = [0.015, 0.030, 0.045, 0.060]\n",
"\n", "\n",
"with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_tv.xlsx', engine='xlsxwriter') as writer:\n", "with pd.ExcelWriter(f'zz800_cyb_{benchmark_code}_tv.xlsx', engine='xlsxwriter') as writer:\n",
...@@ -190,6 +196,18 @@ ...@@ -190,6 +196,18 @@
"language": "python", "language": "python",
"name": "python3" "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": { "varInspector": {
"cols": { "cols": {
"lenName": 16, "lenName": 16,
......
notebooks/Example 6 - Target Volatility Builder.ipynb
View file @ b3973859
...@@ -224,12 +224,7 @@ ...@@ -224,12 +224,7 @@
"\"\"\"\n", "\"\"\"\n",
"Running Settings\n", "Running Settings\n",
"\"\"\"\n", "\"\"\"\n",
"running_setting = RunningSetting(universe,\n", "running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
" rebalance_method='tv',\n", " rebalance_method='tv',\n",
" bounds=bounds,\n", " bounds=bounds,\n",
" target_vol=target_vol)" " target_vol=target_vol)"
...@@ -244,8 +239,15 @@ ...@@ -244,8 +239,15 @@
"\"\"\"\n", "\"\"\"\n",
"Strategy run\n", "Strategy run\n",
"\"\"\"\n", "\"\"\"\n",
"strategy = Strategy(alpha_model, data_meta, running_setting)\n", "strategy = Strategy(alpha_model,\n",
"ret_df, positions = strategy.run()" " 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 @ b3973859
...@@ -63,7 +63,7 @@ ...@@ -63,7 +63,7 @@
"data_source = os.environ['DB_URI']\n", "data_source = os.environ['DB_URI']\n",
"engine = SqlEngine(data_source)\n", "engine = SqlEngine(data_source)\n",
"\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", "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)]" "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 @ b3973859
...@@ -46,7 +46,7 @@ ...@@ -46,7 +46,7 @@
"factor = 'EPS'\n", "factor = 'EPS'\n",
"\n", "\n",
"engine = SqlEngine(os.environ['DB_URI'])\n", "engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['zz800'])" "universe = Universe('zz800')"
] ]
}, },
{ {
......
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