finlab.analysis

finlab.analysis.liquidityAnalysis.LiquidityAnalysis

LiquidityAnalysis(required_volume=200000, required_turnover=1000000)

Bases: Analysis

分析台股策略流動性風險項目的機率

Note

參考VIP限定文章更了解流動性檢測內容細節。

Args: required_volume (int): 要求進出場時的單日成交股數至少要多少？ required_turnover (int): 要求進出場時的單日成交金額至少要多少元？避免成交股數夠，但因低價股因素，造成胃納量仍無法符合資金需求。

Examples:

# better syntax
report.run_analysis('LiquidityAnalysis', required_volume=100000)

# original syntax
from finlab.analysis.liquidityAnalysis import LiquidityAnalysis
report.run_analysis(LiquidityAnalysis(required_volume=100000))

策略流動性檢測

finlab.analysis.inequalityAnalysis.InequalityAnalysis

InequalityAnalysis(name, df=None, date_type='entry_sig_date', target='return')

Bases: Analysis

Analyze return of trades with condition inequality

PARAMETER	DESCRIPTION
`name`	name of the condition TYPE: `str`
`df`	value used in condition. If df is None, `data.get(name)` will be automatically perform to fetch the values. TYPE: `DataFrame or None` DEFAULT: `None`
`date_type`	can be either `entry_date`, `entry_sig_date`, `exit_date`, `exit_sig_date`. TYPE: `str` DEFAULT: `'entry_sig_date'`
`target`	the target to optimize. Any column name in report.get_trades() TYPE: `str` DEFAULT: `'return'`

Examples:

report.run_analysis('InequalityAnalysis', name='price_earning_ratio:股價淨值比')

策略不等式因子分析

finlab.analysis.periodStatsAnalysis.PeriodStatsAnalysis

PeriodStatsAnalysis()

Bases: Analysis

分析台股策略的不同時期與大盤指標作比較

Examples:

可以執行以下程式碼來產生分析結果：

report.run_analysis('PeriodStatsAnalysis')

產生的結果：

	benchmark	strategy
('overall_daily', 'calmar_ratio')	0.149192	0.0655645
('overall_daily', 'sortino_ratio')	0.677986	0.447837
('overall_daily', 'sharpe_ratio')	0.532014	0.306351
('overall_daily', 'profit_factor')	1.20022	1.07741
('overall_daily', 'tail_ratio')	0.914881	0.987751
('overall_daily', 'return')	0.0835801	0.0478957
('overall_daily', 'volatility')	0.182167	0.312543
('overall_monthly', 'calmar_ratio')	0.155321	0.0731378
('overall_monthly', 'sortino_ratio')	0.697382	0.439003
('overall_monthly', 'sharpe_ratio')	0.524943	0.307292
('overall_monthly', 'profit_factor')	1.75714	1.27059
('overall_monthly', 'tail_ratio')	1.03322	0.903335
('overall_monthly', 'return')	0.0836545	0.0479377
('overall_monthly', 'volatility')	0.186989	0.316178
('overall_yearly', 'calmar_ratio')	0.436075	0.127784
('overall_yearly', 'sortino_ratio')	0.738327	0.694786
('overall_yearly', 'sharpe_ratio')	0.407324	0.350986
('overall_yearly', 'profit_factor')	2.2	1.66667
('overall_yearly', 'tail_ratio')	1.71647	1.359
('overall_yearly', 'return')	0.0814469	0.0663674
('overall_yearly', 'volatility')	0.284742	0.419087

假如希望開發交易分析系統，可以繼承 finlab.analysis.Analysis 來實做分析。

finlab.analysis.Analysis

Bases: ABC

analyze

analyze(report)

Analyze trading report.

One could assume self.caluclate_trade_info will be executed before self.analyze, so the report.get_trades() will contain the required trade info.

calculate_trade_info

calculate_trade_info(report)

Additional trade info can be calculated easily.

User could override this function if additional trade info is required for later anlaysis.

Examples:

from finlab.analysis import Analysis

class SomeAnalysis(Analysis):
  def calculate_trade_info(self, report):
    return [
      ['股價淨值比', data.get('price_earning_ratio:股價淨值比'), 'entry_sig_date']
    ]

report.run_analysis(SomeAnalysis())
trades = report.get_trades()

assert '股價淨值比@entry_sig_date' in trades.columns

print(trades)

display

display()

Display result

When implement this function, returning Plotly figure instance is recommended.

is_market_supported

is_market_supported(market)

Check if market info is supported

RETURNS	DESCRIPTION
`bool`	True, support. False not support.