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refactor: update test file to use new standardized method names

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Update test_decision.py to work with refactored core modules.

Changes:
- Removed wildcard imports, using explicit imports:
  - from decisionManager_v2 import DecisionManager
  - from indicators_v2 import ind_BB
  - from signals_v2 import sig_BB
- Updated method calls to use snake_case naming:
  - test.getRetroTrendAns() → test.get_retro_trend_answer()
  - test.generateMatrixProbabilityFromDict() → test.generate_matrix_probability_from_dict()
  - test.getOnlineAns() → test.get_online_answer()
- Updated variable names to snake_case:
  - sigAgrReq → sig_agr_req
  - sigAgrRetroTemplate → sig_agr_retro_template
  - retroAns → retro_ans
  - sigAgrData → sig_agr_data
- Improved spacing and formatting for PEP 8 compliance

The test file now follows the same coding standards as the refactored
core modules and maintains compatibility with all renamed methods.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Mark 2025-11-24 18:38:42 +01:00
parent bfa0d13a25
commit 92eb7db4c5
1 changed files with 33 additions and 32 deletions
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65
market_trade/tests/test_decision.py
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@ -1,63 +1,64 @@
from market_trade.core.decisionManager_v2 import * from market_trade.core.decisionManager_v2 import DecisionManager
from market_trade.core.indicators_v2 import * from market_trade.core.indicators_v2 import ind_BB
from market_trade.core.signals_v2 import * from market_trade.core.signals_v2 import sig_BB
import market_trade.data.dataloader import market_trade.data.dataloader
sigAgrReq = { sig_agr_req = {
'sig_BB':{ 'sig_BB': {
'className':sig_BB, 'className': sig_BB,
'params':{'source':'close','target':'close'}, 'params': {'source': 'close', 'target': 'close'},
'indicators':{ 'indicators': {
'ind_BB':{ 'ind_BB': {
'className':ind_BB, 'className': ind_BB,
'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5} 'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2.5}
} }
} }
}, },
'sig_BB_2':{ 'sig_BB_2': {
'className':sig_BB, 'className': sig_BB,
'params':{'source':'close','target':'close'}, 'params': {'source': 'close', 'target': 'close'},
'indicators':{ 'indicators': {
'ind_BB':{ 'ind_BB': {
'className':ind_BB, 'className': ind_BB,
'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2} 'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2}
} }
} }
} }
} }
test = decsionManager('Pipa', sigAgrReq) test = DecisionManager('Pipa', sig_agr_req)
import pandas as pd import pandas as pd
df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv") df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv")
df_candle["date"] = df_candle["timestamp"] df_candle["date"] = df_candle["timestamp"]
sigAgrRetroTemplate = {
'sig_BB':{ sig_agr_retro_template = {
'sig_BB': {
'signalData': None, 'signalData': None,
'indicatorData' :{'ind_BB': None} 'indicatorData': {'ind_BB': None}
}, },
'sig_BB_2':{ 'sig_BB_2': {
'signalData': None, 'signalData': None,
'indicatorData' :{'ind_BB': None} 'indicatorData': {'ind_BB': None}
} }
} }
retroAns = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[5000:6000].reset_index(drop=True),40) retro_ans = test.get_retro_trend_answer(sig_agr_retro_template, df_candle[5000:6000].reset_index(drop=True), 40)
test.generateMatrixProbabilityFromDict(retroAns) test.generate_matrix_probability_from_dict(retro_ans)
sigAgrData = { sig_agr_data = {
'sig_BB':{ 'sig_BB': {
'signalData': df_candle[990:1000], 'signalData': df_candle[990:1000],
'indicatorData' :{'ind_BB': df_candle[:1000]} 'indicatorData': {'ind_BB': df_candle[:1000]}
}, },
'sig_BB_2':{ 'sig_BB_2': {
'signalData': df_candle[990:1000], 'signalData': df_candle[990:1000],
'indicatorData' :{'ind_BB': df_candle[:1000]} 'indicatorData': {'ind_BB': df_candle[:1000]}
} }
} }
test.getOnlineAns(sigAgrData, 0.0) test.get_online_answer(sig_agr_data, 0.0)