<jupyter_start><jupyter_text>Build a plot out of the recommendation<jupyter_code>simulation_equity = pd.Series(recommended['equity'] + cumsum)
target_equity = recommended['equity'] + pd.Series([average_monthly_net_profit] * len(simulation_equity)).cumsum()
mc_x15_drawdown = recommended['drawdown_pct'] * 1.5
decrease_by_pct = ((100-mc_x15_drawdown)/100)
dollar_x15_drawdown = decrease_by_pct * average_monthly_net_profit
min_equity = recommended['equity'] + pd.Series([dollar_x15_drawdown] * len(simulation_equity)).cumsum()
mdd15 = simulation_equity.cummax() * decrease_by_pct
plt.figure(num=None, figsize=(10, 5), dpi=80, facecolor='w', edgecolor='k')
plt.plot(simulation_equity, color='blue', label='Simulation Equity')
plt.plot(target_equity, color='green', label='Target Equity')
plt.plot(min_equity, color='green', linestyle='--', linewidth=3, label='Min. Equity')
plt.plot(mdd15, color='red', label='MonteCarlo 1.5X Median Drawdown')
plt.legend(loc="upper left")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.153, 'fraction_numerical': 0.028, 'mean_word_length': 10.122222222222222, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '207103', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 395, 'n_tokens_neox': 363, 'n_words': 56, 'path': 'notebooks/2.0-DVR-MonteCarloRiskOfRuin.ipynb', 'repo_name': 'avielfedida/DecisiveML', 'size': '1000'}