<jupyter_start><jupyter_text>Strategy analysis exampleDebugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.  Setup<jupyter_code>from pathlib import Path
from freqtrade.configuration import Configuration

# Customize these according to your needs.

# Initialize empty configuration object
config = Configuration.from_files([])
# Optionally, use existing configuration file
# config = Configuration.from_files(["config.json"])

# Define some constants
config["ticker_interval"] = "5m"
# Name of the strategy class
config["strategy"] = "SampleStrategy"
# Location of the data
data_location = Path(config['user_data_dir'], 'data', 'binance')
# Pair to analyze - Only use one pair here
pair = "BTC_USDT"
# Load data using values set above
from freqtrade.data.history import load_pair_history

candles = load_pair_history(datadir=data_location,
                            timeframe=config["ticker_interval"],
                            pair=pair)

# Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
candles.head()<jupyter_output><empty_output><jupyter_text>Load and run strategy* Rerun each time the strategy file is changed<jupyter_code># Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
strategy = StrategyResolver.load_strategy(config)

# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
df.tail()<jupyter_output><empty_output><jupyter_text>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']  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.<jupyter_code># Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=False)
data.tail()<jupyter_output><empty_output><jupyter_text>Load existing objects into a Jupyter notebookThe 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 dataframeAnalyze a trades dataframe (also used below for plotting)<jupyter_code>from freqtrade.data.btanalysis import load_backtest_data

# Load backtest results
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")

# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()<jupyter_output><empty_output><jupyter_text>Load live trading results into a pandas dataframeIn case you did already some trading and want to analyze your performance<jupyter_code>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()<jupyter_output><empty_output><jupyter_text>Analyze the loaded trades for trade parallelismThis can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.<jupyter_code>from freqtrade.data.btanalysis import analyze_trade_parallelism

# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')

parallel_trades.plot()<jupyter_output><empty_output><jupyter_text>Plot resultsFreqtrade offers interactive plotting capabilities based on plotly.<jupyter_code>from freqtrade.plot.plotting import  generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive

# Filter trades to one pair
trades_red = trades.loc[trades['pair'] == pair]

data_red = data['2019-06-01':'2019-06-10']
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
                                   data=data_red,
                                   trades=trades_red,
                                   indicators1=['sma20', 'ema50', 'ema55'],
                                   indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
                                  )



# Show graph inline
# graph.show()

# Render graph in a seperate window
graph.show(renderer="browser")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0809440218451336, 'fraction_numerical': 0.007021650087770626, 'mean_word_length': 4.873997709049255, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 5, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '36759', 'license': 'MIT', 'n_tokens_mistral': 1498, 'n_tokens_neox': 1419, 'n_words': 493, 'path': 'ft_userdata/user_data/notebooks/strategy_analysis_example.ipynb', 'repo_name': 'Eddi1337/gekko', 'size': '5127'}