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        "description": "****\n# nav_calc\n****\nIt is a lib for backtesting.\n\n## multi_assets\n\n\nThis function is used to test strategies involving mutliple assets, like multi-factors model.\n\n### params:\n\n- prz: dataframe, index = dates, columns = stocks\n\n- w_tar: dataframe, target weight, index = dates, columns = stocks\n\nIt is worthwhile to note that the dates in w_tar does not need to be the same as the dates in prz, it can be a subset of the dates in prz.\n\n- dates_reb: array, rebalance dates\n\nIf you want to balance at every w_tar's dates, set dates_reb to w_tar.index\n\n- fee: fee rate, default = 0\n\n### return a dataframe\n\nIt has the following columns:\n\n- reb: 1 indicate it is a rebalance date, 0 for not a rebalance date\n\n- ret_gross: return before fee\n\n- fee: fees to substract from ret_gross\n\n- ret_net: return after fee\n\n- nav: net asset value of the strategy\n\n\n## time_series\n\nThis function is used to test timing strategies, like CTA strategies. \n\n### params:\n\n- prz: array\n\n- pos: array, indicate the strategy's position\n\nIt must be the same size with prz\n\n- fee: fee rate, default 0\n\n- ret_ext: whether to return the extended information; if False(default), return an array which represents the nav of the strategy; if True, return a DataFrame(see below)\n\n### return a dataframe \n\n**(ret_ext = True)**\n\nIt has the following columns:\n\n- price: the price of the underlying asset\n\n- position: the strategy's position\n\n- return: pct_change of the price\n\n- value: position * price\n\n- fee: trading fees\n\n- strategy_return_before_fee:\n\n- strategy_return: post-fee return\n\n- nav: nav of the strategy (post_fee)\n\n- nav_before_fee: \n\n- nav_price: the normalized price\n\n****\n\n# nav_analysis\n****\nIt is a lib for analysis nav (which maybe from nav_calc).\n\n## calc_mdd\n\nFunction to calculate max drawdown\n\n### params\n\n- arr: an array that represents nav or price\n\n### return a dict\n\nThe dict has the following keys:\n\n- mdd_value: the max drawdown in value\n\n- mdd_pct: the max drawdown in percentage\n\n\n## calc_ann_ret\n\nFunction to calculate the annualized return, assuming daily data.\n\n### parmas\n\n- arr: an array that represents nav or price\n\n### return a value\n\n- annualized return\n\n\n## stats\n\nFunction to return both ret and mdd\n\n### params\n\n- arr: an array that represents nav or price\n\n### return a dict which has the following keys\n\n- ret: the total return (not annualized return)\n\n- mdd: the max drawdown in percentage\n\n## stats_yearly\n\n### params\n\n- s: a series that represents nav or price\n\nThe index of s should be date and must be in the form like \"20181010\".\n\n### return a dataframe which has the following columns:\n\n- ret: the total return (not annualized return)\n\n- mdd: the max drawdown in percentage\n\nThe index of the dataframe is 'year'.\n\n## stats_yearly_multi_assets\n\n### params\n\n- df: a dataframe whose each column represents a nav of an asset or strategy.\n\n### return a dataframe which has two-level columns\n\nLevel I: the asset or strategy\n\nLevel II: ret and mdd, the same meaning as in stats_yearly\n\n****\n# general\n****\n## to_db_code\n\n### params\n- ts_code: security code like '600000.SH'\n\n### return\n- db_code: code format in database like 'SH_600000'\n\n### example\n- to_db_code('600000.SH') returns 'SH_600000'\n\n## to_ts_code\n### params:\n- db_code: code format in database like 'SH_600000'\n\n### return:\n- ts_code: security code like '600000.SH'\n\n### example:\n- to_ts_code('SH_600000') returns '600000.SH'\n\n## real_round_2\nprecise round to 0.01  (\u7528\u4e8e\u8ba1\u7b97\u6da8\u8dcc\u505c\u4ef7)\n\n### param\n- a number\n\n### return \n- number rounded\n\n### example\n-read_round_2(5.347) returns 5.35\n\n## check_df_struct\ncheck if two dataframe have identical struct: same shape, same index and same columns\n\n### params\n- df1, df2\n\n### return\n- True if the two dataframe have the same shape, same index and same columns, else False\n\n### example\ncheck_df_struct(df1, df2)\n\n****\n# utils\n****\n## grouping1d\nassign groupid to an array, used to group stocks according a factor\n\n### params\n- arr: the factor array\n- nog: number of group\n- ST = 999: the number need to be specially treated\n\n### return\nan array that contained groupid, nan will be assigned to the 0 group, ST will be assigned to the -1 group\n\n### example\ngrouping1d(np.arange(100),5)\n\n## get_clean_factor_and_forward_returns\n\n### param\n\n- factor: a mult-index series with index = ['date', 'asset'], value = factor value\n\n- prices: a dataframe with index = daily dates and columns = the universe of stocks interested\n\n- bins = 5: number of group (use bins as the variable name in order to be consistence with alphalens)\n\n- periods = (20,60): the holding periods to be tested\n\n### return\n\na multi-index dataframe with index = ['date', 'asset'] and columns:\n\n- XD(i.e. 20D, 60D,...): 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