sentiment-analysis

Name: sentiment-analysis
Author: agiprolabs

235stars53forks

How to install sentiment-analysis?

agiprolabs/claude-trading-skills/sentiment-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill sentiment-analysis

Installs into the current project.

Summary

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

Files · 1

View on GitHub

SKILL.md

1	# Sentiment Analysis
2
3	Extract and quantify market sentiment from social media, news feeds, and on-chain
4	data to identify crowd positioning and potential contrarian opportunities.
5
6	## When to Use This Skill
7
8	- Gauge crowd sentiment before entering or exiting a position
9	- Detect euphoria/panic extremes that precede reversals
10	- Monitor social mention velocity for early trend detection
11	- Track influencer activity around specific tokens
12	- Build composite sentiment scores for systematic strategies
13
14	## Core Concepts
15
16	### Sentiment Data Sources
17
18	\| Source \| Data Type \| Access \|
19	\|--------\|-----------\|--------\|
20	\| Twitter/X \| Post text, engagement, follower counts \| API (paid tiers) \|
21	\| Reddit \| Subreddit posts, comments, upvotes \| Reddit API \|
22	\| Telegram \| Channel messages, member counts \| Bot API or scraping \|
23	\| Discord \| Server activity, message volume \| Bot integration \|
24	\| News \| Headlines, article text \| NewsAPI, RSS feeds \|
25	\| CoinGecko \| Community stats, developer activity \| Free API \|
26	\| Alternative.me \| Fear & Greed Index \| Free API \|
27	\| On-chain \| Funding rates, exchange flows \| Exchange APIs \|
28
29	See `references/data_sources.md` for complete API details, rate limits, and access
30	patterns for each source.
31
32	### Sentiment Metrics
33
34	Mention Velocity — Rate of token mentions over time:
35
36	```python
37	mention_velocity = mentions_last_hour / baseline_hourly_mentions
38	# > 3.0 = trending, > 10.0 = viral
39	```
40
41	Sentiment Polarity — Positive vs negative tone:
42
43	```python
44	polarity = (positive_count - negative_count) / total_count
45	# Range: -1.0 (all negative) to +1.0 (all positive)
46	```
47
48	Fear & Greed Index — Composite market mood (0-100):
49
50	\| Range \| Label \| Typical Signal \|
51	\|-------\|-------\|----------------\|
52	\| 0-24 \| Extreme Fear \| Potential accumulation zone \|
53	\| 25-44 \| Fear \| Below-average sentiment \|
54	\| 45-55 \| Neutral \| No strong directional bias \|
55	\| 56-74 \| Greed \| Above-average sentiment \|
56	\| 75-100 \| Extreme Greed \| Potential distribution zone \|
57
58	Social Volume — Total mentions across platforms:
59
60	```python
61	social_volume_z = (current_volume - mean_30d) / std_30d
62	# z > 2.0 suggests unusual activity
63	```
64
65	### On-Chain Sentiment Proxies
66
67	On-chain data reveals what participants are doing, not just saying:
68
69	Funding Rates — Perpetual futures cost of carry:
70
71	```python
72	# Positive funding = longs pay shorts (bullish crowding)
73	# Negative funding = shorts pay longs (bearish crowding)
74	funding_sentiment = -1.0 * normalize(funding_rate, -0.1, 0.1)
75	# Inverted: high positive funding is contrarian bearish
76	```
77
78	Long/Short Ratio — Proportion of leveraged positions:
79
80	```python
81	ls_ratio = long_accounts / short_accounts
82	# > 2.0 = crowded long, < 0.5 = crowded short
83	ls_sentiment = -1.0 * normalize(ls_ratio, 0.5, 2.0)
84	```
85
86	Exchange Flows — Net deposits/withdrawals:
87
88	```python
89	net_flow = exchange_inflows - exchange_outflows
90	# Positive net flow (deposits) = bearish (selling pressure)
91	# Negative net flow (withdrawals) = bullish (accumulation)
92	flow_sentiment = -1.0 * normalize(net_flow, -threshold, threshold)
93	```
94
95	### Keyword-Based Sentiment Scoring
96
97	A simple, LLM-free approach using curated word lists:
98
99	```python
100	BULLISH_KEYWORDS = {
101	"moon": 2, "bullish": 2, "pump": 1, "breakout": 2,
102	"buy": 1, "long": 1, "accumulate": 2, "undervalued": 2,
103	"gem": 1, "rocket": 1, "ath": 1, "rally": 2,
104	}
105	BEARISH_KEYWORDS = {
106	"dump": 2, "bearish": 2, "crash": 2, "scam": 3,
107	"rug": 3, "sell": 1, "short": 1, "overvalued": 2,
108	"dead": 2, "rekt": 1, "ponzi": 3, "exit": 1,
109	}
110
111	def score_text(text: str) -> float:
112	"""Score text from -1.0 (bearish) to +1.0 (bullish)."""
113	words = text.lower().split()
114	bull_score = sum(BULLISH_KEYWORDS.get(w, 0) for w in words)
115	bear_score = sum(BEARISH_KEYWORDS.get(w, 0) for w in words)
116	total = bull_score + bear_score
117	if total == 0:
118	return 0.0
119	return (bull_score - bear_score) / total
120	```
121
122	See `references/scoring_methods.md` for the full methodology, temporal decay
123	weighting, and composite score construction.
124
125	### Composite Sentiment Score
126
127	Combine multiple signals into a single score:
128
129	```python
130	def composite_sentiment(
131	social_polarity: float, # -1.0 to +1.0
132	mention_velocity: float, # 0 to inf
133	fear_greed: int, # 0 to 100
134	funding_rate: float, # -0.1 to +0.1
135	weights: dict \| None = None,
136	) -> float:
137	"""Compute weighted composite sentiment score (-100 to +100).
138
139	Args:
140	social_polarity: Average polarity of social mentions.
141	mention_velocity: Current velocity vs baseline.
142	fear_greed: Fear & Greed index reading.
143	funding_rate: Current perpetual funding rate.
144	weights: Optional custom weights.
145
146	Returns:
147	Composite score from -100 (extreme fear) to +100 (extreme greed).
148	"""
149	w = weights or {

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sentiment-analysis

235stars53forks

View on GitHub

How to install sentiment-analysis?

agiprolabs/claude-trading-skills/sentiment-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill sentiment-analysis

Installs into the current project.

Summary

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

Files · 1

View on GitHub

SKILL.md

1	# Sentiment Analysis
2
3	Extract and quantify market sentiment from social media, news feeds, and on-chain
4	data to identify crowd positioning and potential contrarian opportunities.
5
6	## When to Use This Skill
7
8	- Gauge crowd sentiment before entering or exiting a position
9	- Detect euphoria/panic extremes that precede reversals
10	- Monitor social mention velocity for early trend detection
11	- Track influencer activity around specific tokens
12	- Build composite sentiment scores for systematic strategies
13
14	## Core Concepts
15
16	### Sentiment Data Sources
17
18	\| Source \| Data Type \| Access \|
19	\|--------\|-----------\|--------\|
20	\| Twitter/X \| Post text, engagement, follower counts \| API (paid tiers) \|
21	\| Reddit \| Subreddit posts, comments, upvotes \| Reddit API \|
22	\| Telegram \| Channel messages, member counts \| Bot API or scraping \|
23	\| Discord \| Server activity, message volume \| Bot integration \|
24	\| News \| Headlines, article text \| NewsAPI, RSS feeds \|
25	\| CoinGecko \| Community stats, developer activity \| Free API \|
26	\| Alternative.me \| Fear & Greed Index \| Free API \|
27	\| On-chain \| Funding rates, exchange flows \| Exchange APIs \|
28
29	See `references/data_sources.md` for complete API details, rate limits, and access
30	patterns for each source.
31
32	### Sentiment Metrics
33
34	Mention Velocity — Rate of token mentions over time:
35
36	```python
37	mention_velocity = mentions_last_hour / baseline_hourly_mentions
38	# > 3.0 = trending, > 10.0 = viral
39	```
40
41	Sentiment Polarity — Positive vs negative tone:
42
43	```python
44	polarity = (positive_count - negative_count) / total_count
45	# Range: -1.0 (all negative) to +1.0 (all positive)
46	```
47
48	Fear & Greed Index — Composite market mood (0-100):
49
50	\| Range \| Label \| Typical Signal \|
51	\|-------\|-------\|----------------\|
52	\| 0-24 \| Extreme Fear \| Potential accumulation zone \|
53	\| 25-44 \| Fear \| Below-average sentiment \|
54	\| 45-55 \| Neutral \| No strong directional bias \|
55	\| 56-74 \| Greed \| Above-average sentiment \|
56	\| 75-100 \| Extreme Greed \| Potential distribution zone \|
57
58	Social Volume — Total mentions across platforms:
59
60	```python
61	social_volume_z = (current_volume - mean_30d) / std_30d
62	# z > 2.0 suggests unusual activity
63	```
64
65	### On-Chain Sentiment Proxies
66
67	On-chain data reveals what participants are doing, not just saying:
68
69	Funding Rates — Perpetual futures cost of carry:
70
71	```python
72	# Positive funding = longs pay shorts (bullish crowding)
73	# Negative funding = shorts pay longs (bearish crowding)
74	funding_sentiment = -1.0 * normalize(funding_rate, -0.1, 0.1)
75	# Inverted: high positive funding is contrarian bearish
76	```
77
78	Long/Short Ratio — Proportion of leveraged positions:
79
80	```python
81	ls_ratio = long_accounts / short_accounts
82	# > 2.0 = crowded long, < 0.5 = crowded short
83	ls_sentiment = -1.0 * normalize(ls_ratio, 0.5, 2.0)
84	```
85
86	Exchange Flows — Net deposits/withdrawals:
87
88	```python
89	net_flow = exchange_inflows - exchange_outflows
90	# Positive net flow (deposits) = bearish (selling pressure)
91	# Negative net flow (withdrawals) = bullish (accumulation)
92	flow_sentiment = -1.0 * normalize(net_flow, -threshold, threshold)
93	```
94
95	### Keyword-Based Sentiment Scoring
96
97	A simple, LLM-free approach using curated word lists:
98
99	```python
100	BULLISH_KEYWORDS = {
101	"moon": 2, "bullish": 2, "pump": 1, "breakout": 2,
102	"buy": 1, "long": 1, "accumulate": 2, "undervalued": 2,
103	"gem": 1, "rocket": 1, "ath": 1, "rally": 2,
104	}
105	BEARISH_KEYWORDS = {
106	"dump": 2, "bearish": 2, "crash": 2, "scam": 3,
107	"rug": 3, "sell": 1, "short": 1, "overvalued": 2,
108	"dead": 2, "rekt": 1, "ponzi": 3, "exit": 1,
109	}
110
111	def score_text(text: str) -> float:
112	"""Score text from -1.0 (bearish) to +1.0 (bullish)."""
113	words = text.lower().split()
114	bull_score = sum(BULLISH_KEYWORDS.get(w, 0) for w in words)
115	bear_score = sum(BEARISH_KEYWORDS.get(w, 0) for w in words)
116	total = bull_score + bear_score
117	if total == 0:
118	return 0.0
119	return (bull_score - bear_score) / total
120	```
121
122	See `references/scoring_methods.md` for the full methodology, temporal decay
123	weighting, and composite score construction.
124
125	### Composite Sentiment Score
126
127	Combine multiple signals into a single score:
128
129	```python
130	def composite_sentiment(
131	social_polarity: float, # -1.0 to +1.0
132	mention_velocity: float, # 0 to inf
133	fear_greed: int, # 0 to 100
134	funding_rate: float, # -0.1 to +0.1
135	weights: dict \| None = None,
136	) -> float:
137	"""Compute weighted composite sentiment score (-100 to +100).
138
139	Args:
140	social_polarity: Average polarity of social mentions.
141	mention_velocity: Current velocity vs baseline.
142	fear_greed: Fear & Greed index reading.
143	funding_rate: Current perpetual funding rate.
144	weights: Optional custom weights.
145
146	Returns:
147	Composite score from -100 (extreme fear) to +100 (extreme greed).
148	"""
149	w = weights or {

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