Pivot Point Levels and the Challenge of Building Trading Rules
Summary
The document gives formulas for calculating a daily pivot point and the first three support and resistance levels from the prior period's high, low, and close. It shows these levels alongside price bars and existing MACD and moving-average signals, then asks how price location relative to the pivot or support and resistance could guide buy and sell decisions.
The formulas are a commonly used technical-level calculation, but the document does not provide an actual entry, exit, or confirmation rule. It contains no backtest or performance evidence, and the sample data does not establish that crossing or touching a level predicts future returns. Any strategy would need explicit rules and testing, including consideration of the chosen timeframe and trading costs.
Key ideas
- A daily pivot point is calculated from the prior period's high, low, and close.
- Support and resistance levels are derived from the pivot and the prior period's range.
- The example displays pivot levels alongside MACD and moving-average signals.
- The document asks how to turn the levels into buy and sell rules but does not supply a strategy or evidence of effectiveness.
Tags
Full text
# How to use pivot points for a sell/buy order?
# How to use pivot points for a sell/buy order?
I have implemented some trading strategies like macd and sma. When the lines are crossing they give a sell or buy signal.
Now I have calculated pivot points of one day
```
last_day['Pivot'] = (max_value + min_value + close)/3
last_day['R1'] = 2*last_day['Pivot'] - min_value
last_day['S1'] = 2*last_day['Pivot'] - max_value
last_day['R2'] = last_day['Pivot'] + (max_value - min_value)
last_day['S2'] = last_day['Pivot'] - (max_value - min_value)
last_day['R3'] = last_day['Pivot'] + 2*(max_value - min_value)
last_day['S3'] = last_day['Pivot'] - 2*(max_value - min_value)
return last_day
```
my data now looks like this:
```
timestamp open high low close volume macd macds macdh open_5_sma open_10_sma macd_advice msa_advice Pivot
1 2020-10-13T19:30:00.000Z 0.033259 0.033273 0.033223 0.033244 1262.011 -0.000035 -3.931010e-05 4.144859e-06 0.033236 0.033225 HOLD BUY 0.033388
2 2020-10-13T19:45:00.000Z 0.033244 0.033248 0.033211 0.033211 1068.909 -0.000036 -3.858108e-05 2.916057e-06 0.033239 0.033224 HOLD HOLD 0.033388
3 2020-10-13T20:00:00.000Z 0.033211 0.033216 0.033181 0.033211 1329.222 -0.000036 -3.799490e-05 2.344739e-06 0.033235 0.033219 HOLD HOLD 0.033388
4 2020-10-13T20:15:00.000Z 0.033211 0.033231 0.033192 0.033194 1109.637 -0.000037 -3.771359e-05 1.125225e-06 0.033238 0.033221 HOLD HOLD 0.033388
5 2020-10-13T20:30:00.000Z 0.033193 0.033199 0.033161 0.033165 597.887 -0.000039 -3.801485e-05 -1.205013e-06 0.033224 0.033226 SELL SELL 0.033388
```
In the data above I've only added Pivots now, I'm able to add S1, S2, S3 and R1, R2, R3
How can I create a buy/sell strategy using this data? I don't need code of course but just someone who can explain when you know when you have to buy or sell something depending on the position of the current price depending on the pivot point (or R1/S1).Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.