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Matthias
2019-08-05 06:55:51 +02:00
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122
docs/data-analysis.md
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@@ -6,6 +6,128 @@ A good way for this is using Jupyter (notebook or lab) - which provides an inter
The following helpers will help you loading the data into Pandas DataFrames, and may also give you some starting points in analyzing the results.
## Strategy development problem analysis
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.
It's recommended 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.
```python
# Some necessary imports
from pathlib import Path
from freqtrade.data.history import load_pair_history
from freqtrade.resolvers import StrategyResolver
# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategyname = 'Awesomestrategy'
# Location of the strategy
strategy_location = '../xmatt/strategies'
# Location of the data
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,
pair=pair)
print(len(bt_data))
### Start strategy reload
# Load strategy - best done in a new cell
# Rerun each time the strategy-file is changed.
strategy = StrategyResolver({'strategy': strategyname,
'user_data_dir': Path.cwd(),
'strategy_path': location}).strategy
# Run strategy (just like in backtesting)
df = strategy.analyze_ticker(bt_data, {'pair': pair})
print(f"Generated {df['buy'].sum()} buy signals")
# Reindex data to be "nicer" and show data
data = df.set_index('date', drop=True)
data.tail()
```
### Explanation
#### Imports and constant definition
``` python
# Some necessary imports
from pathlib import Path
from freqtrade.data.history import load_pair_history
from freqtrade.resolvers import StrategyResolver
# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategyname = 'Awesomestrategy'
# Location of the strategy
strategy_location = 'user_data/strategies'
# Location of the data
data_location = 'user_data/data/binance'
# Only use one pair here
pair = "XRP_ETH"
```
This first section imports necessary modules, and defines some constants you'll probably need to adjust for your case.
#### Load candles
``` python
# Load data
bt_data = load_pair_history(datadir=Path(data_location),
ticker_interval = ticker_interval,
pair=pair)
print(len(bt_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
Now, it's time to load and run your strategy.
For this, I recommend using a new cell in your notebook, since you'll want to repeat this until you're satisfied with your strategy.
``` python
# Load strategy - best done in a new cell
# Needs to be ran each time the strategy-file is changed.
strategy = StrategyResolver({'strategy': strategyname,
'user_data_dir': Path.cwd(),
'strategy_path': location}).strategy
# Run strategy (just like in backtesting)
df = strategy.analyze_ticker(bt_data, {'pair': pair})
print(f"Generated {df['buy'].sum()} buy signals")
# Reindex data to be "nicer" and show data
data = df.set_index('date', drop=True)
data.tail()
```
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.
Note that using `data.head()` would also work, however this is misleading since most indicators have some "startup" time at the start of a backtested dataframe.
There can be many things wrong, some signs to look for are:
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
## Backtesting
To analyze your backtest results, you can [export the trades](#exporting-trades-to-file).

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docs/faq.md
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@@ -45,6 +45,16 @@ the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-c
You can use the `/forcesell all` command from Telegram.
### I get the message "RESTRICTED_MARKET"
Currently known to happen for US Bittrex users.
Bittrex split its exchange into US and International versions.
The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
## Hyperopt module
### How many epoch do I need to get a good Hyperopt result?

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docs/hyperopt.md
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@@ -303,8 +303,10 @@ Given the following result from hyperopt:
```
Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
@@ -347,21 +349,15 @@ If you are optimizing ROI, you're result will look as follows and include a ROI
```
Best result:
135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins.
with values:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': false,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower',
'roi_t1': 40,
'roi_t2': 57,
'roi_t3': 21,
'roi_p1': 0.03634636907306948,
'roi_p2': 0.055237357937802885,
'roi_p3': 0.015163796015548354,
'stoploss': -0.37996664668703606
}
'trigger': 'bb_lower'}
ROI table:
{ 0: 0.10674752302642071,
21: 0.09158372701087236,
@@ -372,9 +368,9 @@ ROI table:
This would translate to the following ROI table:
``` python
minimal_roi = {
minimal_roi = {
"118": 0,
"78": 0.0363463,
"78": 0.0363,
"21": 0.0915,
"0": 0.106
}