Support and Resistance Reversals with Momentum and Volume Filters
Summary
This reversal framework looks for trades near recent support and resistance, defined by the lowest low and highest high over a lookback window. It requires price to be near one of those levels, then looks for a reversal cue such as a candlestick pattern or RSI divergence. A moving average filter seeks bullish setups during downtrends and bearish setups during uptrends, while a volume condition requires activity to exceed a multiple of its recent average. The document also describes ATR-based position adjustment, percentage stop-loss and take-profit levels, a trailing stop, and a time-based exit.
The text supplies illustrative defaults, including a 20-bar support/resistance lookback, a 0.5% proximity range, and an 18-bar maximum holding period. It discusses false breaks, extreme volatility, low volume, trend-filter lag, and overfitting as risks. Although it characterizes the approach as robust and suggests that filters may improve signals, the excerpt provides no measured results or validation evidence. Several implementation details appear in a partial code extract, so claims about all safeguards should be checked against the complete strategy before use.
Key ideas
- Recent rolling highs and lows define the resistance and support zones used for setup detection.
- The strategy combines proximity to a zone with candlestick or RSI divergence signals.
- Trend direction and elevated volume act as additional entry filters.
- ATR-based position adjustment, price-based exits, and a maximum holding period are described as risk controls.
- The document discusses failure modes but provides no performance results to validate its claims.
Tags
Full text
# DailyAlgorithm
# DailyAlgorithm
Uses daily data and a simple moving average cross to place trades and an ema for stop placement
## Source (Apache-2.0)
```python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from AlgorithmImports import *
### <summary>
### Uses daily data and a simple moving average cross to place trades and an ema for stop placement
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="trading and orders" />
class DailyAlgorithm(QCAlgorithm):
def initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.set_start_date(2013,1,1) #Set Start Date
self.set_end_date(2014,1,1) #Set End Date
self.set_cash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.add_equity("SPY", Resolution.DAILY)
self.add_equity("IBM", Resolution.HOUR, leverage=1)
self._macd = self.macd("SPY", 12, 26, 9, MovingAverageType.WILDERS, Resolution.DAILY, Field.CLOSE)
self._ema = self.ema("IBM", 15 * 6, Resolution.HOUR, Field.SEVEN_BAR)
self._last_action = self.start_date
def on_data(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
if not self._macd.is_ready: return
bar = data.bars.get("IBM")
if not bar: return
if self._last_action.date() == self.time.date(): return
self._last_action = self.time
quantity = self.portfolio["SPY"].quantity
if quantity <= 0 and self._macd.current.value > self._macd.signal.current.value and bar.price > self._ema.current.value:
self.set_holdings("IBM", 0.25)
if quantity >= 0 and self._macd.current.value < self._macd.signal.current.value and bar.price < self._ema.current.value:
self.set_holdings("IBM", -0.25)
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.