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docs/strategy_analysis_example.md

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+## Strategy debugging example
+
+Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
+
+## Setup
+
+```python
+# Change directory
+# Modify this cell to insure that the output shows the correct path.
+import os
+from pathlib import Path
+
+# Define all paths relative to the project root shown in the cell output
+project_root = "somedir/freqtrade"
+i=0
+try:
+    os.chdirdir(project_root)
+    assert Path('LICENSE').is_file()
+except:
+    while i<4 and (not Path('LICENSE').is_file()):
+        os.chdir(Path(Path.cwd(), '../'))
+        i+=1
+    project_root = Path.cwd()
+print(Path.cwd())
+```
+
+
+```python
+# Customize these according to your needs.
+
+# Define some constants
+ticker_interval = "5m"
+# Name of the strategy class
+strategy_name = 'SampleStrategy'
+# Path to user data
+user_data_dir = 'user_data'
+# Location of the strategy
+strategy_location = Path(user_data_dir, 'strategies')
+# Location of the data
+data_location = Path(user_data_dir, 'data', 'binance')
+# Pair to analyze - Only use one pair here
+pair = "BTC_USDT"
+```
+
+
+```python
+# Load data using values set above
+from pathlib import Path
+from freqtrade.data.history import load_pair_history
+
+candles = load_pair_history(datadir=data_location,
+                            ticker_interval=ticker_interval,
+                            pair=pair)
+
+# Confirm success
+print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
+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
+strategy = StrategyResolver({'strategy': strategy_name,
+                            'user_data_dir': user_data_dir,
+                            'strategy_path': strategy_location}).strategy
+
+# 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")
+data = df.set_index('date', drop=True)
+data.tail()
+```
+
+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.