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# Strategy analysis example
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Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
## Setup
```python
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from pathlib import Path
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from freqtrade.configuration import Configuration
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# Customize these according to your needs.
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# Initialize empty configuration object
config = Configuration.from_files([])
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# Optionally, use existing configuration file
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# config = Configuration.from_files(["config.json"])
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# Define some constants
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config["ticker_interval"] = "5m"
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# Name of the strategy class
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config["strategy"] = "SampleStrategy"
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# Pair to analyze - Only use one pair here
pair = "BTC_USDT"
```
```python
# Load data using values set above
from freqtrade.data.history import load_pair_history
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candles = load_pair_history(datadir=config["datadir"],
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timeframe=config["ticker_interval"],
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pair=pair)
# Confirm success
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print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {config['datadir']}")
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candles.head()
```
## Load and run strategy
* Rerun each time the strategy file is changed
```python
# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
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strategy = StrategyResolver.load_strategy(config)
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# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
df.tail()
```
### Display the trade details
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
* Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
* Comparison with full backtest
* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
```python
# Report results
print(f"Generated {df['buy'].sum()} buy signals")
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data = df.set_index('date', drop=False)
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data.tail()
```
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## Load existing objects into a Jupyter notebook
The following cells assume that you have already generated data using the cli.
They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
### Load backtest results to pandas dataframe
Analyze a trades dataframe (also used below for plotting)
```python
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
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trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
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# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()
```
### Load live trading results into a pandas dataframe
In case you did already some trading and want to analyze your performance
```python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
trades.groupby("pair")["sell_reason"].value_counts()
```
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## Analyze the loaded trades for trade parallelism
This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions` .
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`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
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```python
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from freqtrade.data.btanalysis import analyze_trade_parallelism
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# Analyze the above
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parallel_trades = analyze_trade_parallelism(trades, '5m')
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parallel_trades.plot()
```
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## Plot results
Freqtrade offers interactive plotting capabilities based on plotly.
```python
from freqtrade.plot.plotting import generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive
data_red = data['2019-06-01':'2019-06-10']
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)
```
```python
# Show graph inline
# graph.show()
# Render graph in a seperate window
graph.show(renderer="browser")
```
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Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
