Mark
92eb7db4c5
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>
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
from market_trade.core.decisionManager_v2 import DecisionManager
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from market_trade.core.indicators_v2 import ind_BB
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from market_trade.core.signals_v2 import sig_BB
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import market_trade.data.dataloader
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sig_agr_req = {
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'sig_BB': {
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'className': sig_BB,
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'params': {'source': 'close', 'target': 'close'},
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'indicators': {
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'ind_BB': {
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'className': ind_BB,
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'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2.5}
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}
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}
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},
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'sig_BB_2': {
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'className': sig_BB,
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'params': {'source': 'close', 'target': 'close'},
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'indicators': {
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'ind_BB': {
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'className': ind_BB,
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'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2}
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}
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}
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}
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}
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test = DecisionManager('Pipa', sig_agr_req)
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import pandas as pd
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df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv")
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df_candle["date"] = df_candle["timestamp"]
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sig_agr_retro_template = {
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'sig_BB': {
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'signalData': None,
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'indicatorData': {'ind_BB': None}
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},
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'sig_BB_2': {
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'signalData': None,
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'indicatorData': {'ind_BB': None}
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}
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}
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retro_ans = test.get_retro_trend_answer(sig_agr_retro_template, df_candle[5000:6000].reset_index(drop=True), 40)
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test.generate_matrix_probability_from_dict(retro_ans)
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sig_agr_data = {
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'sig_BB': {
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'signalData': df_candle[990:1000],
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'indicatorData': {'ind_BB': df_candle[:1000]}
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},
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'sig_BB_2': {
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'signalData': df_candle[990:1000],
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'indicatorData': {'ind_BB': df_candle[:1000]}
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}
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}
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test.get_online_answer(sig_agr_data, 0.0)
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