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softwaredevelop merged 1 commit into from
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feat: sandbox_sma_implementation_comparison.ipynb #8

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319 changes: 319 additions & 0 deletions notebooks/sandbox_sma_implementation_comparison.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "7d40767d",
"metadata": {},
"outputs": [],
"source": [
"# filename: sandbox_sma_implementation_comparison.ipynb\n",
"\n",
"# --- Imports and Setup ---\n",
"import numpy as np\n",
"import pandas as pd\n",
"import vectorbt as vbt\n",
"from numba import njit\n",
"\n",
"# --- Data Loading ---\n",
"symbol = \"EURUSD=X\"\n",
"end_date = pd.Timestamp.now(tz=\"UTC\")\n",
"start_date = end_date - pd.Timedelta(days=729)\n",
"timeframe = \"1h\"\n",
"\n",
"print(\n",
" f\"Downloading {symbol} {timeframe} data from {start_date.date()} to {end_date.date()}...\"\n",
")\n",
"try:\n",
" ohlc_data = vbt.YFData.download(\n",
" symbol, start=start_date, end=end_date, interval=timeframe\n",
" ).get([\"Open\", \"High\", \"Low\", \"Close\"])\n",
"\n",
" if ohlc_data.empty:\n",
" raise ValueError(\"No data returned from yfinance.\")\n",
"\n",
" close_price = ohlc_data[\"Close\"]\n",
" print(\"Data download complete.\\n\")\n",
"\n",
"except Exception as e:\n",
" print(f\"An error occurred during data download: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4145c6c",
"metadata": {},
"outputs": [],
"source": [
"# --- Method 1: TA-Lib ---\n",
"print(\"--- Running Optimization using 'TA-Lib' ---\")\n",
"\n",
"# 1. Create indicator factory\n",
"sma_factory_talib = vbt.IndicatorFactory.from_talib(\"SMA\")\n",
"\n",
"# 2. Define parameter ranges\n",
"fast_sma_range = np.arange(10, 30, 2)\n",
"slow_sma_range = np.arange(40, 60, 2)\n",
"\n",
"# 3. Calculate indicators\n",
"fast_sma_talib = sma_factory_talib.run(\n",
" close_price, timeperiod=fast_sma_range, short_name=\"fast\"\n",
")\n",
"slow_sma_talib = sma_factory_talib.run(\n",
" close_price, timeperiod=slow_sma_range, short_name=\"slow\"\n",
")\n",
"\n",
"# 4. Generate signals\n",
"entries = fast_sma_talib.real.vbt.crossed_above(slow_sma_talib.real)\n",
"exits = fast_sma_talib.real.vbt.crossed_below(slow_sma_talib.real)\n",
"\n",
"# 5. Run portfolio backtest\n",
"portfolio_talib = vbt.Portfolio.from_signals(\n",
" close_price, entries, exits, freq=timeframe, init_cash=10000\n",
")\n",
"\n",
"# 6. Analyze and display results\n",
"if portfolio_talib.trades.count().sum() == 0:\n",
" print(\"No trades were executed for any parameter combination.\")\n",
"else:\n",
" # Find the best parameter combination\n",
" best_params_talib = portfolio_talib.sharpe_ratio().idxmax()\n",
"\n",
" # Select the single best portfolio instance\n",
" best_portfolio = portfolio_talib[best_params_talib]\n",
"\n",
" # Get the stats for that single portfolio\n",
" best_stats_talib = best_portfolio.stats()\n",
"\n",
" print(f\"Best Parameters (fast_period, slow_period): {best_params_talib}\")\n",
" print(\"Performance for Best Parameters:\")\n",
" print(\n",
" best_stats_talib[\n",
" [\"Total Return [%]\", \"Sharpe Ratio\", \"Max Drawdown [%]\", \"Win Rate [%]\"]\n",
" ]\n",
" )\n",
"\n",
" # 7. Visualize the best strategy\n",
" # --- CORRECTED, LAYERED PLOTTING LOGIC ---\n",
"\n",
" # Get the indicator series for the best parameters\n",
" best_fast_sma = fast_sma_talib.real[best_params_talib[0]]\n",
" best_slow_sma = slow_sma_talib.real[best_params_talib[1]]\n",
"\n",
" # Step A: Create the base candlestick chart from the OHLC data\n",
" fig = ohlc_data.vbt.ohlc.plot(\n",
" title_text=f\"TA-Lib SMA Strategy | Best Params: {best_params_talib}\",\n",
" template=\"plotly_dark\",\n",
" )\n",
"\n",
" # Step B: Add the SMA lines to the existing figure\n",
" best_fast_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Fast SMA\", line=dict(color=\"cyan\"))\n",
" )\n",
" best_slow_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Slow SMA\", line=dict(color=\"orange\"))\n",
" )\n",
"\n",
" # Step C: Add only the trade markers to the existing figure\n",
" best_portfolio.trades.plot(fig=fig)\n",
"\n",
" # Step D: Show the final, combined figure\n",
" fig.show()\n",
"\n",
"print(\"-\" * 50 + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cc35354",
"metadata": {},
"outputs": [],
"source": [
"# --- Method 2: pandas-ta ---\n",
"print(\"--- Running Optimization using 'pandas-ta' ---\")\n",
"\n",
"# 1. Create indicator factory, naming the output 'sma'\n",
"# This tells vectorbt to create a '.sma' attribute on the output object\n",
"sma_factory_pdta = vbt.IndicatorFactory.from_pandas_ta(\"sma\", output_names=[\"sma\"])\n",
"\n",
"# 2. Define parameter ranges (using the same as before for comparison)\n",
"fast_sma_range = np.arange(10, 30, 2)\n",
"slow_sma_range = np.arange(40, 60, 2)\n",
"\n",
"# 3. Calculate indicators\n",
"# pandas-ta's period parameter is named 'length'\n",
"fast_sma_pdta = sma_factory_pdta.run(\n",
" close_price, length=fast_sma_range, short_name=\"fast\"\n",
")\n",
"slow_sma_pdta = sma_factory_pdta.run(\n",
" close_price, length=slow_sma_range, short_name=\"slow\"\n",
")\n",
"\n",
"# 4. Generate signals using the '.sma' output attribute\n",
"entries = fast_sma_pdta.sma.vbt.crossed_above(slow_sma_pdta.sma)\n",
"exits = fast_sma_pdta.sma.vbt.crossed_below(slow_sma_pdta.sma)\n",
"\n",
"# 5. Run portfolio backtest\n",
"portfolio_pdta = vbt.Portfolio.from_signals(\n",
" close_price, entries, exits, freq=timeframe, init_cash=10000\n",
")\n",
"\n",
"# 6. Analyze and display results\n",
"if portfolio_pdta.trades.count().sum() == 0:\n",
" print(\"No trades were executed for any parameter combination.\")\n",
"else:\n",
" # Find the best parameter combination\n",
" best_params_pdta = portfolio_pdta.sharpe_ratio().idxmax()\n",
"\n",
" # Select the single best portfolio instance\n",
" best_portfolio = portfolio_pdta[best_params_pdta]\n",
"\n",
" # Get the stats for that single portfolio\n",
" best_stats_pdta = best_portfolio.stats()\n",
"\n",
" print(f\"Best Parameters (fast_period, slow_period): {best_params_pdta}\")\n",
" print(\"Performance for Best Parameters:\")\n",
" print(\n",
" best_stats_pdta[\n",
" [\"Total Return [%]\", \"Sharpe Ratio\", \"Max Drawdown [%]\", \"Win Rate [%]\"]\n",
" ]\n",
" )\n",
"\n",
" # 7. Visualize the best strategy using the same layered approach\n",
"\n",
" # Get the indicator series for the best parameters\n",
" best_fast_sma = fast_sma_pdta.sma[best_params_pdta[0]]\n",
" best_slow_sma = slow_sma_pdta.sma[best_params_pdta[1]]\n",
"\n",
" # Step A: Create the base candlestick chart\n",
" fig = ohlc_data.vbt.ohlc.plot(\n",
" title_text=f\"pandas-ta SMA Strategy | Best Params: {best_params_pdta}\",\n",
" template=\"plotly_dark\",\n",
" )\n",
"\n",
" # Step B: Add the SMA lines\n",
" best_fast_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Fast SMA\", line=dict(color=\"cyan\"))\n",
" )\n",
" best_slow_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Slow SMA\", line=dict(color=\"orange\"))\n",
" )\n",
"\n",
" # Step C: Add the trade markers\n",
" best_portfolio.trades.plot(fig=fig)\n",
"\n",
" # Step D: Show the final figure\n",
" fig.show()\n",
"\n",
"print(\"-\" * 50 + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecf78ab3",
"metadata": {},
"outputs": [],
"source": [
"# --- Method 3: Custom Implementation (Numba Accelerated) ---\n",
"print(\"--- Running Optimization using 'Custom Numba Function' ---\")\n",
"\n",
"# # Import Numba and vectorbt's numba-aware functions\n",
"# import vectorbt as vbt\n",
"\n",
"\n",
"# CORRECTED: The custom function must handle 2D numpy arrays.\n",
"# We use vectorbt's built-in, numba-accelerated rolling_mean_nb for this.\n",
"# The @njit decorator compiles this function for massive speedup.\n",
"@njit\n",
"def custom_sma_func_nb(close, n):\n",
" \"\"\"Calculates SMA using vectorbt's numba-accelerated rolling mean.\"\"\"\n",
" # vbt.nb.rolling_mean_nb correctly handles the 2D array passed by the factory\n",
" return vbt.nb.rolling_mean_nb(close, window=n, minp=n)\n",
"\n",
"\n",
"# 1. Create the indicator class from our numba-jitted function\n",
"CustomSMA = vbt.IndicatorFactory(\n",
" input_names=[\"close\"], param_names=[\"n\"], output_names=[\"sma\"]\n",
").from_apply_func(\n",
" custom_sma_func_nb # Use the new, numba-aware function\n",
")\n",
"\n",
"# 2. Define parameter ranges\n",
"fast_sma_range = np.arange(10, 30, 2)\n",
"slow_sma_range = np.arange(40, 60, 2)\n",
"\n",
"# 3. Calculate indicators\n",
"fast_sma_custom = CustomSMA.run(close_price, n=fast_sma_range, short_name=\"fast\")\n",
"slow_sma_custom = CustomSMA.run(close_price, n=slow_sma_range, short_name=\"slow\")\n",
"\n",
"# 4. Generate signals\n",
"entries = fast_sma_custom.sma.vbt.crossed_above(slow_sma_custom.sma)\n",
"exits = fast_sma_custom.sma.vbt.crossed_below(slow_sma_custom.sma)\n",
"\n",
"# 5. Run portfolio backtest\n",
"portfolio_custom = vbt.Portfolio.from_signals(\n",
" close_price, entries, exits, freq=timeframe, init_cash=10000\n",
")\n",
"\n",
"# 6. Analyze and display results\n",
"if portfolio_custom.trades.count().sum() == 0:\n",
" print(\"No trades were executed for any parameter combination.\")\n",
"else:\n",
" best_params_custom = portfolio_custom.sharpe_ratio().idxmax()\n",
" best_portfolio = portfolio_custom[best_params_custom]\n",
" best_stats_custom = best_portfolio.stats()\n",
"\n",
" print(f\"Best Parameters (fast_period, slow_period): {best_params_custom}\")\n",
" print(\"Performance for Best Parameters:\")\n",
" print(\n",
" best_stats_custom[\n",
" [\"Total Return [%]\", \"Sharpe Ratio\", \"Max Drawdown [%]\", \"Win Rate [%]\"]\n",
" ]\n",
" )\n",
"\n",
" # 7. Visualize the best strategy\n",
" best_fast_sma = fast_sma_custom.sma[best_params_custom[0]]\n",
" best_slow_sma = slow_sma_custom.sma[best_params_custom[1]]\n",
"\n",
" fig = ohlc_data.vbt.ohlc.plot(\n",
" title_text=f\"Custom Numba SMA Strategy | Best Params: {best_params_custom}\",\n",
" template=\"plotly_dark\",\n",
" )\n",
" best_fast_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Fast SMA\", line=dict(color=\"cyan\"))\n",
" )\n",
" best_slow_sma.vbt.plot(\n",
" fig=fig, trace_kwargs=dict(name=\"Slow SMA\", line=dict(color=\"orange\"))\n",
" )\n",
" best_portfolio.trades.plot(fig=fig)\n",
" fig.show()\n",
"\n",
"print(\"-\" * 50 + \"\\n\")"
]
}
],
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