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Update README.md
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README.md
10
README.md
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<div align="center">
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<br>
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<img src="docs/docs/assets/logo/header.svg">
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<img src="docs/docs/assets/images/logo/header.svg">
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<br>
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</div>
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<br>
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@@ -194,7 +194,7 @@ fig = mean_expectancy.unstack().vbt.scatterplot(xaxis_title='randnx_n', yaxis_ti
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fig.show()
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```
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* For fans of hyperparameter optimization: here is a snippet for testing 10,000 window combinations of a
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dual SMA crossover strategy on BTC, USD, and LTC:
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@@ -217,7 +217,7 @@ fig = pf.total_return().vbt.heatmap(
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fig.show()
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```
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<img width="650" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/usage_dmac_heatmap.gif">
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<img width="650" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_dmac_heatmap.gif">
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Digging into each strategy configuration is as simple as indexing with pandas:
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@@ -263,7 +263,7 @@ The same for plotting:
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pf[(10, 20, 'ETH-USD')].plot().show()
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```
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It's not all about backtesting - vectorbt can be used to facilitate financial data analysis and visualization.
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@@ -297,7 +297,7 @@ vbt.save_animation('bbands.gif', bbands.wrapper.index, plot, bbands, delta=90, s
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100%|██████████| 31/31 [00:21<00:00, 1.21it/s]
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```
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<img width="750" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/usage_bbands.gif">
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<img width="750" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_bbands.gif">
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And this is just the tip of the iceberg of what's possible. Check out [Resources](#resources) to learn more.
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