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+61.59 BCH +28.5% Upbit
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Top 30 coins at 2022-01-15 19:01:18
↘️-0.11 Bitcoin BTC $43375.29 $821105078073
↘️-0.21 Ethereum ETH $3346.76 $398842935936
↗️+0.17 BNB BNB $499.32 $83287865128
↘️-0.04 Tether USDT $1 $78426864944
↘️-0.54 Solana SOL $147.67 $46368387547
↗️+0.01 USD Coin USDC $1 $45271411561
↗️+0.01 Cardano ADA $1.28 $42959615989
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↘️-0.3 Polygon MATIC $2.38 $17404710921
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↗️+0.09 Litecoin LTC $148.39 $10301559362
↗️+0.01 Dai DAI $1 $9531384311
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↘️-0.28 Algorand ALGO $1.4 $9057101948
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↘️-0.13 Stellar XLM $0.26 $6483113334
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Market capitalization: $ 2078 billion (+ 7.3%) 🔺 (against $ 2075 billion yesterday morning).
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There are several libraries available that ease the process of calculating technical indicators. We’ve already discussed how you can export a DataFrame as a CSV file. There’s a lot more you can do with Pandas and calculating a moving average is one of them. Here’s an example:, the top 10 cryptocurrency stellar exchange s. In the above code, we loaded our data from the CSV file we created earlier. Then we use the mean() function to calculate the average on the close column. The rolling function allows us to set a period for the moving average. This all gets appended to the existing DataFrame. This is what the result looks like. As you can see, a new column has been created with the 20 moving average. Let’s say you only need to know where the moving average is as of right now. Or as of the last price point in the DataFrame. We can use the same mean() function and just run it on the last 20 rows of the DataFrame like this: There is a lot more Pandas can do. 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