CSV Export | Smart Swing Tools Docs

The chart shows you structure visually. CSV export gives you the raw data behind it — every swing, every cycle count, every hierarchy position — for analysis in spreadsheets, databases, Python, R, or any tool you prefer.

SSTP exports 63 columns of hierarchical swing data through TradingView's native "Export chart data" functionality. Unlike SSI PRO's single-timeframe export, SSTP's export captures the state of all 10 timeframes simultaneously on every bar, giving you a complete multi-timeframe dataset in one file.

How to Export

Open your chart with SSTP applied
In the indicator settings, navigate to Group ⑨ (CSV Export)
Enable "CSV Export"
Right-click anywhere on the chart
Select "Export chart data"
Choose your date range and save the CSV file

The export includes every bar in the selected range. On bars where no swing occurred, the event columns (Swing_Event, Swing_Type) contain NaN/empty values while the state columns retain their last-known values.

Right-click the chart and select "Export chart data" to download the CSV file containing all 63 SSTP columns.

The 63 Columns

The export is organized into two categories: per-timeframe columns (6 columns × 10 timeframes = 60) and global columns (3).

Per-Timeframe Columns

Each of the 10 timeframes exports 6 columns using the naming pattern {TF}_{Field}:

Column	Type	What It Contains
`{TF}_Cycle`	Integer	The hierarchical cycle count — how many swings at this timeframe since the last parent-timeframe swing reset the counter. Resets to 1 when a parent swing confirms.
`{TF}_Phase`	Integer	`1` = last swing was a High (market turned down), `0` = last swing was a Low (market turned up). Combined with Cycle, tells you both position and direction within the parent cycle.
`{TF}_Price`	Float	The price at the most recent swing extreme for this timeframe. Updates when a new swing is confirmed.
`{TF}_Time`	Unix timestamp	When the most recent swing extreme occurred. This is the timestamp of the bar where the high or low actually happened, not where it was confirmed.
`{TF}_Dir`	Integer	The current swing direction: `1` = trending up (last swing was a low, price is moving higher), `-1` = trending down (last swing was a high, price is moving lower).
`{TF}_TDays`	Integer	Trading days elapsed since the previous swing at this timeframe. Only populated for Daily and intraday timeframes. Shows NaN for Weekly and above (use Time to calculate duration for those).

Timeframe Prefixes

The 10 timeframe prefixes, from highest to lowest:

Prefix	Timeframe	Example Column
`Y`	Yearly	Y_Cycle, Y_Phase, Y_Price, Y_Time, Y_Dir, Y_TDays
`Q`	Quarterly	Q_Cycle, Q_Phase, Q_Price, Q_Time, Q_Dir, Q_TDays
`M`	Monthly	M_Cycle, M_Phase, M_Price, M_Time, M_Dir, M_TDays
`W`	Weekly	W_Cycle, W_Phase, W_Price, W_Time, W_Dir, W_TDays
`3D`	3-Day	3D_Cycle, 3D_Phase, 3D_Price, 3D_Time, 3D_Dir, 3D_TDays
`2D`	2-Day	2D_Cycle, 2D_Phase, 2D_Price, 2D_Time, 2D_Dir, 2D_TDays
`D`	Daily	D_Cycle, D_Phase, D_Price, D_Time, D_Dir, D_TDays
`4H`	4-Hour	4H_Cycle, 4H_Phase, 4H_Price, 4H_Time, 4H_Dir, 4H_TDays
`1H`	1-Hour	1H_Cycle, 1H_Phase, 1H_Price, 1H_Time, 1H_Dir, 1H_TDays
`15m`	15-Minute	15m_Cycle, 15m_Phase, 15m_Price, 15m_Time, 15m_Dir, 15m_TDays

Global Columns

Three columns capture cross-timeframe information:

Column	Type	What It Contains
`Full_Hierarchy`	Integer	The odometer — a single number encoding all 10 cycle counts. Each digit position represents one timeframe's count (Y in billions, Q in hundred millions, down to 15m in ones). Decode by extracting digits.
`Swing_Event`	Integer	Which timeframe fired a swing on this bar. Uses rank indices: 0 = 15m, 1 = 1H, 2 = 4H, 3 = D, 4 = 2D, 5 = 3D, 6 = W, 7 = M, 8 = Q, 9 = Y. NaN on bars with no swing event.
`Swing_Type`	Integer	Direction of the swing that fired: `1` = Swing High, `-1` = Swing Low. NaN on bars with no swing event.

Understanding the Data

State Columns vs. Event Columns

This distinction is critical for working with the data correctly.

State columns (_Cycle, _Phase, _Price, _Time, _Dir, _TDays) persist their values across bars. Once a Weekly swing high confirms and sets W_Phase to 1, that value remains 1 on every subsequent bar until the next Weekly swing changes it. These columns represent the current state of each timeframe — what's true right now.

Event columns (Swing_Event, Swing_Type) are populated only on bars where a swing was confirmed. On all other bars, they contain NaN. These columns mark the moments when something happened.

Practical consequence: If you filter the CSV to rows where Swing_Event is not NaN, you get a table of all swing events with their full hierarchical context. If you read the data row-by-row, the state columns give you a snapshot of all 10 timeframes at every bar.

Reading the Odometer (Full_Hierarchy)

The Full_Hierarchy column encodes all 10 cycle counts into a single integer using positional notation:

Full_Hierarchy = Y×10⁹ + Q×10⁸ + M×10⁷ + W×10⁶ + 3D×10⁵ + 2D×10⁴ + D×10³ + 4H×10² + 1H×10¹ + 15m×10⁰

Example: Full_Hierarchy = 1203007014

Position	Digit	Meaning
Billions	1	Y_Cycle = 1 (1st yearly swing)
Hundred millions	2	Q_Cycle = 2 (2nd quarterly swing)
Ten millions	0	M_Cycle = 0 (no monthly swing yet)
Millions	3	W_Cycle = 3 (3rd weekly swing)
Hundred thousands	0	3D_Cycle = 0 (no 3D swing yet)
Ten thousands	0	2D_Cycle = 0 (no 2D swing yet)
Thousands	7	D_Cycle = 7 (7th daily swing)
Hundreds	0	4H_Cycle = 0 (no 4H swing yet)
Tens	1	1H_Cycle = 1 (1st hourly swing)
Ones	4	15m_Cycle = 4 (4th 15m swing)

To decode in Python:

python

def decode_hierarchy(value):
    s = str(int(value)).zfill(10)
    labels = ['Y', 'Q', 'M', 'W', '3D', '2D', 'D', '4H', '1H', '15m']
    return {labels[i]: int(s[i]) for i in range(10)}

# decode_hierarchy(1203007014)
# → {'Y': 1, 'Q': 2, 'M': 0, 'W': 3, '3D': 0, '2D': 0, 'D': 7, '4H': 0, '1H': 1, '15m': 4}

Note: The odometer uses single digits per position, so cycle counts above 9 at any level will overflow into the adjacent position. This is rare in practice — a timeframe would need 10+ swings before its parent resets the counter — but be aware of it when decoding very long datasets.

Reading Phase + Dir Together

_Phase and _Dir are related but subtly different:

_Phase tells you the direction of the last confirmed swing: 1 = the last swing was a High, 0 = the last swing was a Low
_Dir tells you the current trend direction: 1 = trending up (last swing was Low, so price is now heading up), -1 = trending down

They're inverses: when Phase = 1 (last swing was High), Dir = -1 (now trending down). When Phase = 0 (last swing was Low), Dir = 1 (now trending up).

The redundancy is intentional — depending on your analysis, one framing may be more intuitive than the other.

CSV Data Only Mode

For deep historical exports, SSTP offers a mode that bypasses TradingView's 500-label drawing limit.

The problem: TradingView limits visual labels to 500 per indicator instance. On a daily chart with 50 years of data and 4 active timeframes, you'd hit this limit quickly — meaning the indicator would only show labels for recent history.

The solution: Enable "CSV Data Only (No Labels)" in Group ⑨. This runs all swing detection, classification, hierarchy tracking, and CSV state computation on every bar — but skips label and line drawing entirely. Without visual objects consuming the 500-label budget, the indicator processes the full chart history.

What you get: A complete CSV with all 63 columns covering the entire visible date range, including decades of data on instruments with long histories (like major stock indices or commodities).

What you lose: No labels or lines appear on the chart. The chart looks as if the indicator isn't applied (except for the settings panel being visible). This mode is purely for data extraction.

When to use it: Building historical databases, backtesting across decades, academic research on cycle behavior, or any workflow where you need complete data and don't need visual chart output.

Working with the Data

Filtering to Swing Events

Most rows contain only state data — no new swing occurred on that bar. To extract just the swing events:

Python:

python

import pandas as pd

df = pd.read_csv('export.csv')
swings = df[df['Swing_Event'].notna()]

Excel / Google Sheets: Filter the Swing_Event column to exclude blanks/NaN.

swings <- subset(data, !is.na(Swing_Event))

The resulting table contains one row per swing event, with the full hierarchical state at the moment each swing confirmed.

Filtering by Timeframe

To extract swings from a specific timeframe, filter Swing_Event by its rank index:

python

# Weekly swings only (rank 6)
weekly_swings = df[df['Swing_Event'] == 6]

# Monthly swings only (rank 7)
monthly_swings = df[df['Swing_Event'] == 7]

Rank	Timeframe
0	15-Minute
1	1-Hour
2	4-Hour
3	Daily
4	2-Day
5	3-Day
6	Weekly
7	Monthly
8	Quarterly
9	Yearly

Converting Timestamps

The _Time columns use Unix timestamps (seconds since January 1, 1970). Convert to readable dates:

Python:

python

from datetime import datetime

timestamp = 1731801600
date = datetime.utcfromtimestamp(timestamp)
# → 2024-11-17 00:00:00

JavaScript:

javascript

const timestamp = 1731801600;
const date = new Date(timestamp * 1000);

Excel:

=A1/86400 + DATE(1970,1,1)

Format the cell as Date/Time.

Building a Swing Timeline

Combine Swing_Event, Swing_Type, and the relevant timeframe columns to build a chronological swing timeline:

python

swings = df[df['Swing_Event'].notna()].copy()

tf_map = {0: '15m', 1: '1H', 2: '4H', 3: 'D', 4: '2D', 5: '3D', 6: 'W', 7: 'M', 8: 'Q', 9: 'Y'}
swings['Timeframe'] = swings['Swing_Event'].map(tf_map)
swings['Direction'] = swings['Swing_Type'].map({1: 'High', -1: 'Low'})

# Get the price for each swing from its timeframe column
swings['Price'] = swings.apply(
    lambda row: row[f"{tf_map[int(row['Swing_Event'])]}_Price"], axis=1
)

timeline = swings[['time', 'Timeframe', 'Direction', 'Price']].reset_index(drop=True)

Detecting Cycle Resets

When a parent timeframe confirms a swing, all child counters reset. You can detect these resets by watching for drops in cycle counts:

python

# Detect weekly cycle resets (triggered by monthly swings)
df['W_Cycle_prev'] = df['W_Cycle'].shift(1)
resets = df[(df['W_Cycle'] == 1) & (df['W_Cycle_prev'] > 1)]

Each reset row corresponds to a bar where a Monthly (or higher) swing confirmed and reset the Weekly counter to 1.

Measuring Cycle Maturity

The cycle count tells you where you are within a parent cycle. If historical Monthly cycles typically contain 8–12 Weekly swings, and the current W_Cycle is 11, the cycle is mature — a Monthly-level reversal may be approaching.

python

# Weekly cycle lengths (number of weekly swings per monthly cycle)
monthly_swings = df[df['Swing_Event'] == 7]
cycle_lengths = monthly_swings['W_Cycle'].tolist()

print(f"Average weekly swings per monthly cycle: {sum(cycle_lengths) / len(cycle_lengths):.1f}")
print(f"Range: {min(cycle_lengths)} to {max(cycle_lengths)}")

SSTP + SSI PRO Dual Export

If you run both SSTP and SSI PRO on the same chart, both indicators contribute their columns to the CSV export. This gives you the best of both worlds:

SSTP columns (63): Multi-timeframe hierarchy, cycle counts, cross-timeframe state
SSI PRO columns: Single-timeframe detail — ExtremeTimestamp vs ConfirmTimestamp, pattern codes, bar index

The combined export lets you cross-reference single-timeframe swing detail (from SSI PRO) with multi-timeframe context (from SSTP) in the same dataset.

Workflow:

Apply both indicators to the same chart
Enable CSV Export in both indicators' settings
Right-click → Export chart data
The resulting CSV contains columns from both indicators plus TradingView's standard OHLCV

Running SSTP and SSI PRO together provides the most comprehensive swing data export — multi-timeframe hierarchy plus single-timeframe detail in one file.

Building a Swing Database

CSV export is ideal for initial database population or periodic full refreshes.

Suggested Schema

sql

CREATE TABLE sstp_swings (
    id SERIAL PRIMARY KEY,
    symbol VARCHAR(20),
    bar_time TIMESTAMP,
    timeframe VARCHAR(5),
    swing_type INTEGER,
    swing_price DECIMAL(18, 8),
    swing_time BIGINT,
    cycle_count INTEGER,
    phase INTEGER,
    direction INTEGER,
    full_hierarchy BIGINT,
    created_at TIMESTAMP DEFAULT NOW(),

    UNIQUE(symbol, bar_time, timeframe)
);

Import Workflow

Export historical data — Use CSV Data Only mode for full history
Parse the CSV — Filter to rows where Swing_Event is not NaN
Map timeframe ranks — Convert Swing_Event integers to timeframe strings
Extract per-timeframe fields — Pull the relevant {TF}_Cycle, {TF}_Phase, {TF}_Price, {TF}_Time for the active timeframe
Insert rows — One row per swing event with the full hierarchical context
Add indexes — On symbol, timeframe, swing_time for query performance

Keeping It Updated

CSV export captures a snapshot in time. For ongoing updates, you have two options:

Periodic re-export: Export weekly or monthly and upsert new rows. The unique constraint prevents duplicates.

Manual addition: As new swings confirm on the chart, add them to the database manually or through a scripted workflow.

Complete Column Reference

All 63 columns in export order, from highest timeframe to lowest:

#	Column	Type	Category
1	Y_Cycle	Integer	Yearly state
2	Y_Phase	Integer	Yearly state
3	Y_Price	Float	Yearly state
4	Y_Time	Unix timestamp	Yearly state
5	Y_Dir	Integer	Yearly state
6	Y_TDays	Integer	Yearly state
7	Q_Cycle	Integer	Quarterly state
8	Q_Phase	Integer	Quarterly state
9	Q_Price	Float	Quarterly state
10	Q_Time	Unix timestamp	Quarterly state
11	Q_Dir	Integer	Quarterly state
12	Q_TDays	Integer	Quarterly state
13	M_Cycle	Integer	Monthly state
14	M_Phase	Integer	Monthly state
15	M_Price	Float	Monthly state
16	M_Time	Unix timestamp	Monthly state
17	M_Dir	Integer	Monthly state
18	M_TDays	Integer	Monthly state
19	W_Cycle	Integer	Weekly state
20	W_Phase	Integer	Weekly state
21	W_Price	Float	Weekly state
22	W_Time	Unix timestamp	Weekly state
23	W_Dir	Integer	Weekly state
24	W_TDays	Integer	Weekly state
25	3D_Cycle	Integer	3-Day state
26	3D_Phase	Integer	3-Day state
27	3D_Price	Float	3-Day state
28	3D_Time	Unix timestamp	3-Day state
29	3D_Dir	Integer	3-Day state
30	3D_TDays	Integer	3-Day state
31	2D_Cycle	Integer	2-Day state
32	2D_Phase	Integer	2-Day state
33	2D_Price	Float	2-Day state
34	2D_Time	Unix timestamp	2-Day state
35	2D_Dir	Integer	2-Day state
36	2D_TDays	Integer	2-Day state
37	D_Cycle	Integer	Daily state
38	D_Phase	Integer	Daily state
39	D_Price	Float	Daily state
40	D_Time	Unix timestamp	Daily state
41	D_Dir	Integer	Daily state
42	D_TDays	Integer	Daily state
43	4H_Cycle	Integer	4-Hour state
44	4H_Phase	Integer	4-Hour state
45	4H_Price	Float	4-Hour state
46	4H_Time	Unix timestamp	4-Hour state
47	4H_Dir	Integer	4-Hour state
48	4H_TDays	Integer	4-Hour state
49	1H_Cycle	Integer	1-Hour state
50	1H_Phase	Integer	1-Hour state
51	1H_Price	Float	1-Hour state
52	1H_Time	Unix timestamp	1-Hour state
53	1H_Dir	Integer	1-Hour state
54	1H_TDays	Integer	1-Hour state
55	15m_Cycle	Integer	15-Min state
56	15m_Phase	Integer	15-Min state
57	15m_Price	Float	15-Min state
58	15m_Time	Unix timestamp	15-Min state
59	15m_Dir	Integer	15-Min state
60	15m_TDays	Integer	15-Min state
61	Full_Hierarchy	Integer	Global
62	Swing_Event	Integer	Global
63	Swing_Type	Integer	Global

Next Steps

Tooltips & Metrics — The same data shown visually in chart tooltips
Settings — CSV Export settings (Group ⑨)
Overview — How CSV Export fits into the broader SSTP system
Glossary — Definitions for all terms used in this page