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Matthias
2019-08-02 19:50:12 +02:00
parent 01cd30984b
commit 0b9b5f3993
1 changed files with 6 additions and 3 deletions
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docs/data-analysis.md
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@@ -11,7 +11,7 @@ The following helpers will help you loading the data into Pandas DataFrames, and
Debugging a strategy (are there no buy signals, ...) can be very time-consuming.
FreqTrade tries to help you by exposing a few helper-functions, which can be very handy.
I recommend using Juptyer Notebooks for this analysis, since it offers a dynamic way to rerun certain parts.
It's recommendet using Juptyer Notebooks for analysis, since it offers a dynamic way to rerun certain parts of the code.
The following is a full code-snippet, which will be explained by both comments, and step by step below.
@@ -33,6 +33,8 @@ data_location = '../freqtrade/user_data/data/binance/'
# Only use one pair here
pair = "XRP_ETH"
### End constants
# Load data
bt_data = load_pair_history(datadir=Path(data_location),
ticker_interval = ticker_interval,
@@ -91,7 +93,8 @@ bt_data = load_pair_history(datadir=Path(data_location),
print(len(bt_data))
```
This second section loads the historic data and prints the amount of candles in the data.
This second section loads the historic data and prints the amount of candles in the DataFrame.
You can also inspect this dataframe by using `bt_data.head()` or `bt_data.tail()`.
#### Run strategy and analyze results
@@ -114,7 +117,7 @@ data = df.set_index('date', drop=True)
data.tail()
```
The code snippet loads and analyzes the strategy, prints the number of buy signals.
The code snippet loads and analyzes the strategy, calculates and prints the number of buy signals.
The last 2 lines serve to analyze the dataframe in detail.
This can be important if your strategy did not generate any buy signals.