diff options
Diffstat (limited to 'python/mark_backtest_underpar.py')
| -rw-r--r-- | python/mark_backtest_underpar.py | 87 |
1 files changed, 30 insertions, 57 deletions
diff --git a/python/mark_backtest_underpar.py b/python/mark_backtest_underpar.py index 401ef81c..821d86a7 100644 --- a/python/mark_backtest_underpar.py +++ b/python/mark_backtest_underpar.py @@ -5,13 +5,11 @@ import matplotlib.pyplot as plt import statsmodels.api as sm import seaborn as sb import datetime +import globeop_reports as ops from statsmodels.formula.api import gls - from utils.db import serenitas_engine, dawn_engine, DataError -import globeop_reports as ops - def get_mark_df(asset_class="Subprime"): # It used to be >1000 but as of 12/17/2018 changed it to 3000 @@ -22,7 +20,10 @@ def get_mark_df(asset_class="Subprime"): df_trades = pd.DataFrame() for date in df_external_marks.date.unique(): df_temp = pd.read_sql_query( - "select description, identifier, usd_market_value/price*100 as curr_ntl from risk_positions(%s, %s) where price >0 and length(identifier) = 9 ", + "select description, identifier, " + "usd_market_value/price*100 as curr_ntl " + "from risk_positions(%s, %s) " + "where price >0 and length(identifier) = 9 ", dawn_engine, params=[date, asset_class], ) @@ -32,20 +33,21 @@ def get_mark_df(asset_class="Subprime"): return df.set_index(["date", "identifier"]) -def calc_mark_diff( - df, - sources=[ - "PRICESERVE", - "PRICINGDIRECT", - "BVAL", - "MARKIT", - "BROKER", - "REUTERS", - "S&P", - "IDC", - ], -): +def calc_mark_diff(df=None, sources=None): + if sources is None: + sources = [ + "PRICESERVE", + "PRICINGDIRECT", + "BVAL", + "MARKIT", + "BROKER", + "REUTERS", + "S&P", + "IDC", + ] + if df is None: + df = get_mark_df() # All Sources (including manager...?!) - average, manager mark only, median, closest g = df.groupby(level=["date", "identifier"]) diff = g.mean() @@ -72,11 +74,8 @@ def calc_mark_diff( def closest(x): - if x.mark.count() > 1: - x["dist"] = abs(x.mark - x.mark[x.source == "MANAGER"]) - return x.mark[x.dist == x.dist[x.dist > 0].min()].iloc[0] - else: - return x.mark[0] + x["dist"] = abs(x.mark - x.mark[x.source == "MANAGER"]).fillna(0) + return x.mark[x.dist == x.dist.min()].iloc[0] def remove_max_min(x): @@ -86,8 +85,10 @@ def remove_max_min(x): return x.mean() -def diff_by_source(df): +def diff_by_source(df=None): # diff_by_source: input get_mark_df(), calculate the pricing by source + if df is None: + df = get_mark_df() df = df.drop("description", 1) df = df.set_index(["source"], append=True).apply( lambda x: x.curr_ntl * x.mark / 100, axis=1 @@ -97,24 +98,11 @@ def diff_by_source(df): return df.groupby(level="date").sum() -def diff_by_source_percentage(df): - df = diff_by_source(df) - df = df.join(ops.get_net_navs()["endbooknav"]) - df = df.apply(lambda x: (x / x.endbooknav), axis=1) - del df["endbooknav"] - return df - - -def count_sources(df): - # input get_mark_df(), plot count of each source - g2 = df.set_index("source", append=True).groupby(level=["date", "source"]) - # there are a good amount of Bloomberg duplicates, not a big deal but should clean them up - g2["mark"].count().unstack(-1).plot() - - -def alt_navs(): - navs = ops.get_net_navs() - df = calc_mark_diff(get_mark_df()) +def alt_navs(df=None): + navs = ops.get_net_navs().sort_index() + if df is None: + df = get_mark_df() + df = calc_mark_diff(df) end_nav, beg_nav, returns, nav_100 = ( pd.DataFrame(), pd.DataFrame(), @@ -125,28 +113,13 @@ def alt_navs(): end_nav[col] = df[col] + navs.endbooknav beg_nav[col] = end_nav[col].shift(1) + navs.net_flow.shift(1) beg_nav[col].iloc[0] = 12500000 - returns[col] = (end_nav[col] - navs.incentive) / beg_nav[col] - 1 + returns[col] = (end_nav[col] - navs.incentive_fee) / beg_nav[col] - 1 for i, row in returns.dropna().reset_index().iterrows(): nav_100.iloc[i] = 100 if i == 0 else nav_100.iloc[i - 1] nav_100.iloc[i] = nav_100.iloc[i] * (1 + returns.iloc[i]) return returns, nav_100 -def annual_performance(nav_100): - perf = nav_100.groupby(pd.Grouper(freq="A")).last() - perf_ann = perf / perf.shift(1) - 1 - perf_ann["2013"] = perf["2013"] / 100 - 1 - return perf_ann - - -def alt_nav_impact(): - navs = ops.get_net_navs() - df = calc_mark_diff(get_mark_df()) - df = df.join(navs.endbooknav) - return df - # return df.iloc[-1]/df.iloc[-1]['endbooknav'] - - def back_test( begindate="2013-01-01", enddate=datetime.date.today(), sell_price_threshold=200 ): |